Showing papers by "Zhao Yang Dong published in 2020"
TL;DR: An intensive summary of several detection algorithms for false data injection attacks by categorizing them and elaborating on the pros and cons of each category is provided.
Abstract: Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.
362 citations
TL;DR: The capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed and it is shown that the proposed method outperforms several state-of-the-art approaches in termsof accuracy, adaptability, and robustness under diverse operating conditions.
Abstract: State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.
121 citations
TL;DR: A detailed model for the IPGS is presented with the consideration of the power-to-gas devices and gas storages, and the gas storage life reliability model is considered to characterize the charging and discharging performance.
Abstract: The reliability evaluation of integrated power-gas systems (IPGS) becomes critical due to the high dependency of the two energy systems. Once a contingent incident happens in one system, the other system will accordingly be affected. In this paper, a detailed model for the IPGS is presented with the consideration of the power-to-gas devices and gas storages. Furthermore, a sequential Monte Carlo (SMC) simulation is utilized to evaluate the reliability of the IPGS. In particular, the gas storage life reliability model is considered to characterize the charging and discharging performance. Moreover, an optimal load shedding model is used to coordinate the load shedding of the IPGS. What's more, new reliability indices are given to display the reliability of the IPGS. Finally, the proposed model is tested on an integrated IEEE 24-bus power system and 20-node gas system and an integrated IEEE RTS 96 power system and 40-node gas system. The results show the effectiveness of the proposed model.
119 citations
TL;DR: This review highlights details of ESS sizing to optimize storage capacity, reduce consumption, minimize storage cost, determine the optimal placement and mitigate carbon emission for decarbonization.
Abstract: Carbon emission from the burning of fossil fuel has resulted in global warming Climate change and global warming are among the most complex issues requiring immediate solutions Microgrid (MG) based on renewable energy sources (RESs) can be used to reduce the carbon intensity of electricity and achieve the global decarbonization goal by 2050 Optimizing the size of the energy storage system (ESS) can ensure the sustainable, resilient, and economic operation of the MG Thus, key features of the optimal ESS, including methods and algorithms of ESS sizing, power quality, reliability, connection mode, and public policy enforcement for low-carbon emission, must be identified Existing literature mostly focuses on the cost-effective optimal sizing method based on capacity minimization, which overlooks other issues This work reviews the features of optimal ESS sizing methods and algorithms, their characteristics, and the scenarios between ESS and decarbonization in MG applications to address their shortcomings ESS characteristics on storage type, energy density, efficiency, advantages, and issues are analyzed This review highlights details of ESS sizing to optimize storage capacity, reduce consumption, minimize storage cost, determine the optimal placement and mitigate carbon emission for decarbonization The analyses on the understanding of decarbonization in relation to the use of ESS in MG scenarios are explained rigorously Existing research gaps, issues, and challenges of ESS sizing for next-generation MG development are also highlighted This review will strengthen the efforts of researchers and industrialists to develop an optimally sized ESS for future MGs that can contribute toward achieving the decarbonization goal
116 citations
TL;DR: This paper presents a practical and effective non-intrusive load monitoring (NILM) solution to estimate the energy consumption for common multi-functional home appliances (type II appliances) that includes a novel post-processing technique that boost the performance significantly on type II home appliances.
Abstract: This paper presents a practical and effective non-intrusive load monitoring (NILM) solution to estimate the energy consumption for common multi-functional home appliances (type II appliances). Type II home appliances are usually the most challenging cases in load disaggregation because they usually have multiple power consumption states, complex state transitions, and multiple operational modes. The practicality of the proposed deep convolutional neural networks-based approach comes from the minimum prerequisite information from the previously unseen customers. That means no sub-metered information for the target appliances in the NILM service subscriber’s house is needed to provide appliance level identification and estimate under the proposed architecture. Our solution also includes a novel post-processing technique that boost the performance significantly on type II home appliances. The effectiveness of the solution is evaluated on a public dataset to allow comparison with the previous works.
113 citations
TL;DR: A review of levelized cost of electricity for renewable generation considering aspects such as improving traditional methods for evaluating and reducing the LCOE of VRE and proposing suggestions for future research studies.
Abstract: Levelized cost of electricity (LCOE) is widely used to compare the cost of different electricity generation technologies. However, with the increasing penetration of variable renewable energy (VRE), it is inappropriate to use traditional equations to calculate the LCOE for non-dispatchable VRE due to its intermittent nature. Therefore, this paper reviews LCOE for renewable generation considering aspects such as improving traditional methods for evaluating and reducing the LCOE of VRE. The existing methods for improving the accuracy of the traditional LCOE have been divided into four aspects: investment-related cost, operating-related cost, expressions for plant performance and the uncertainty and risk-related costs. The review summarizes the existing studies from academic articles and technical reports and proposes suggestions for future research studies.
107 citations
TL;DR: To address the uncertain renewable energy in the day-ahead optimal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to minimize the expected total costs and to deal with the “Curse of Dimensionality” of stochastically programming.
Abstract: To address the uncertain renewable energy in the day-ahead optimal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to minimize the expected total costs. The uncertainties over the multiple stages are characterized by a scenario tree and the optimal dispatch scheme is cast as a decision tree which guarantees the flexibility to decide the reasonable outputs of generation and the adequate reserves accounting for different realizations of renewable energy. Most importantly, to deal with the “Curse of Dimensionality” of stochastic programming, stochastic dual dynamic programming (SDDP) is employed, which decomposes the original problem into several sub-problems according to the stages. Specifically, the SDDP algorithm performs forward pass and backward pass repeatedly until the convergence criterion is satisfied. At each iteration, the original problem is approximated by creating a linear piecewise function. Besides, an improved convergence criterion is adopted to narrow the optimization gaps. The results on the IEEE 118-bus system and real-life provincial power grid show the effectiveness of the proposed model and method.
95 citations
TL;DR: A new home energy management system (HEMS) is proposed, which optimally schedules the operation of home energy resources, with the aim to minimize the home’s one-day electricity cost charged by the real-time pricing while taking into account the monthly basis peak power consumption penalty.
Abstract: Two-way communication facilities and advanced metering infrastructure enable residential buildings to be capable of actively participating in demand side management schemes. This paper proposes a new home energy management system (HEMS), which optimally schedules the operation of home energy resources, with the aim to minimize the home’s one-day electricity cost charged by the real-time pricing while taking into account the monthly basis peak power consumption penalty, charged by the demand charge tariff. To better ensure the user’s lifestyle requirements, the HEMS also models lifestyle-related operational dependencies of household appliances. The numerical simulations and case studies are conducted to validate the reasonability of the proposed method.
82 citations
TL;DR: A two-stage coordination approach of price-based demand response (PBDR) and battery energy storage systems (BESS) to minimize the total operating cost and enhance operational reliability and results indicate the high energy utilization efficiency and strong operational reliability of the proposed coordination approach.
Abstract: Microgrids can effectively integrate distributed generation (DG) to supply power to local loads. However, uncertainties from renewable DG and loads may lead to increased operating costs or operating constraint violations. To solve these issues, this paper proposes a two-stage coordination approach of price-based demand response (PBDR) and battery energy storage systems (BESSs) to minimize the total operating cost and enhance operational reliability. In the first stage, day-ahead PBDR is scheduled, aiming to shift loads to improve renewable energy utilization efficiency. Considering limited prediction horizon of uncertainties when dispatching BESSs, hourly state of charge (SoC) limits are also optimized over the whole-day horizon with consideration of BESS degradation cost in the day-ahead stage. Then in the second stage, the BESSs are dispatched hourly within the optimized SoC limits to track uncertainty realization and compensate the first-stage PBDR decisions. Furthermore, a two-stage interval optimization (TSIO) method is proposed to formulate the problem. Accordingly, a solution algorithm is developed to coordinately solve the two operation stages under the uncertainties. The proposed coordination approach is verified with uncertainty realization scenarios. The results indicate the high energy utilization efficiency and strong operational reliability of the proposed coordination approach.
80 citations
TL;DR: An alternating optimization procedure integrating a column-and-constraint generation algorithm and an alternating direction method of multipliers to solve the DAR-VVC problem is developed.
Abstract: This paper proposes a distributed adaptive robust voltage/var control (DAR-VVC) method in active distribution networks to minimize power loss while keeping operating constraints under uncertainties. The DAR-VVC aims to coordinate on-load tap changers, capacitor banks and PV inverters in multiple operation stages through a distributed algorithm. To improve efficiency of the distributed algorithm, an affinity propagation clustering algorithm is employed to divide the distribution network by aggregating “the close nodes” together and setting “the far nodes” apart, leading to the network partition where the information exchange between adjacent sub-networks is reduced. Moreover, the virtual load which describes load characteristics of the sub-networks is applied to enhance the boundary conditions. To fully deal with the uncertainties, the proposed DAR-VVC is formulated in a robust optimization model which considers the worst case to guarantee solution robustness against uncertainty realization. Besides, this paper develops an alternating optimization procedure integrating a column-and-constraint generation algorithm and an alternating direction method of multipliers to solve the DAR-VVC problem. The proposed approach is tested on IEEE 33 and IEEE 123 bus distribution test system and numerical simulations verify high efficiency and full solution robustness of the DAR-VVC.
80 citations
01 Mar 2020
TL;DR: A human-centred learning-oriented smart campus is envisaged, defined and framed up, primarily aiming at meeting stakeholders’ interests and elevating educational performance in pace of the technology development, as well as discussing the interdisciplinary factors that either promote or constrain the smart campus revolution.
Abstract: As the high-end form of a smart education system, the smart campus has received increasing research attention over the world. Owing to the multidisciplinary nature of the smart campus, the existing research is mostly one-ended on either the state-or-the-art technologies or the innovative education concepts but lacks a deep fusion view on them and omits the smart campus implication on other smart city domains. This study highlights the interdisciplinary view on smart campus. Based on an integral review on the supporting technologies and existing smart campus propositions, a human-centred learning-oriented smart campus is envisaged, defined and framed up, primarily aiming at meeting stakeholders’ interests and elevating educational performance in pace of the technology development, as well as discussing the interdisciplinary factors that either promote or constrain the smart campus revolution. The expected contribution throughout this study is to provide a benchmark reference of a smart campus for international educational providers, government, and technological companies providing such services.
TL;DR: A robust optimization method to guarantee optimal and reliable multi-energy supply under the uncertainties is developed and results show that it can achieve high energy utilization efficiency and high operating robustness against the uncertainties.
Abstract: A multi-energy micro-grid (MEMG) consists of combined cooling, heat and power generation units, thermal energy storage systems, diesel generators, and renewable energy generators and it can simultaneously supply electric and thermal loads. The MEMG can operate in grid-connected or islanded mode depending on practical needs. However, uncertainties existing in the renewable power generation and multi-energy loads pose significant challenges to the operation of the MEMGs in terms of economic profits and conformity with operating constraints. To address the uncertainties, this paper proposes a robustly coordinated operation approach which coordinates multiple devices in different timescales to minimize the operating costs. Both the operation modes and the practical operating constraints are considered. In addition, this paper develops a robust optimization method to guarantee optimal and reliable multi-energy supply under the uncertainties. The proposed approach is verified and compared with existing methods, and simulation results show that it can achieve high energy utilization efficiency and high operating robustness against the uncertainties.
TL;DR: The knowledge-assisted deep deterministic policy gradient algorithm and three kinds of knowledge- assisted learning methods are proposed based on the framework and the simulation results show that the KA-DDPG algorithm can reach the maximum power output and ensure safety during learning.
Abstract: Cooperative wind farm control is a complex problem due to wake effect, and it is hard to find the proper model. Reinforcement learning can find the optimal policy in a dynamic environment using “trial and error,” but may damage the machine and cause high cost during the learning process. In order to address this challenge, this article proposes the knowledge-assisted reinforcement learning framework by combining the low-fidelity analytical model with a reinforcement learning framework. Moreover, the knowledge-assisted deep deterministic policy gradient (KA-DDPG) algorithm and three kinds of knowledge-assisted learning methods are proposed based on the framework. The proposed methods are tested in nine different scenarios of WFSim. The simulation results show that the KA-DDPG algorithm can reach the maximum power output and ensure safety during learning. In addition, the learning cost is reduced by accelerating the learning process.
TL;DR: A guide subspace learning (GSL) method for UDA, in which an invariant, discriminative, and domain-agnostic subspace is learned by three guidance terms through a two-stage progressive training strategy, which outperform many state-of-the-art UDA methods.
Abstract: A prevailing problem in many machine learning tasks is that the training (i.e., source domain) and test data (i.e., target domain) have different distribution [i.e., non-independent identical distribution (i.i.d.)]. Unsupervised domain adaptation (UDA) was proposed to learn the unlabeled target data by leveraging the labeled source data. In this article, we propose a guide subspace learning (GSL) method for UDA, in which an invariant, discriminative, and domain-agnostic subspace is learned by three guidance terms through a two-stage progressive training strategy. First, the subspace-guided term reduces the discrepancy between the domains by moving the source closer to the target subspace. Second, the data-guided term uses the coupled projections to map both domains to a unified subspace, where each target sample can be represented by the source samples with a low-rank coefficient matrix that can preserve the global structure of data. In this way, the data from both domains can be well interlaced and the domain-invariant features can be obtained. Third, for improving the discrimination of the subspaces, the label-guided term is constructed for prediction based on source labels and pseudo-target labels. To further improve the model tolerance to label noise, a label relaxation matrix is introduced. For the solver, a two-stage learning strategy with teacher teaches and student feedbacks mode is proposed to obtain the discriminative domain-agnostic subspace. In addition, for handling nonlinear domain shift, a nonlinear GSL (NGSL) framework is formulated with kernel embedding, such that the unified subspace is imposed with nonlinearity. Experiments on various cross-domain visual benchmark databases show that our methods outperform many state-of-the-art UDA methods. The source code is available at https://github.com/Fjr9516/GSL .
TL;DR: The proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac–dc MG operation cost while all of the constraints of the network are satisfied.
Abstract: This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac–dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac–dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac–dc MG operation cost while all of the constraints of the network are satisfied.
TL;DR: The thermal inertia aggregation model (TIAM) is proposed, which offers an accurate DHN and buildings model for the planning and operation of IESs and reveals its advantages in the computational efficiency and sensitive information protection of DHN.
Abstract: 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.
TL;DR: A novel method for the whole-life-cycle planning of BESS for providing multiple functional services in power systems aims to balance between extending BESS life duration and maximizing its overall revenue by strategically allocates battery capacity for each application over its whole life cycle.
Abstract: One battery energy storage system (BESS) can provide multiple services to support electrical grid. However, the investment return, technical performance and lifetime degradation differ widely among different services. This paper proposes a novel method for the whole-life-cycle planning of BESS for providing multiple functional services in power systems. The proposed model aims to balance between extending BESS life duration and maximizing its overall revenue by strategically allocates battery capacity for each application over its whole life cycle. Specifically, BESS are initially used in ancillary service market for frequency regulation service; after certain lifetime degradation, the used batteries are transferred in energy arbitrage market for load shifting service. BESS depreciation along with the services is considered to accurately model the battery degradation effect under different marketing rules. The proposed method is tested on a modified IEEE 33-bus radial distribution system. Numerical results demonstrate that the proposed method can achieve higher economic benefits and longer life span than a single application service.
TL;DR: An expansion planning model for distribution networks by considering multiple types of energy resources in distribution side, including shared electric vehicle (SEV) charging stations, solar-based distributed generation sources, and battery energy storage systems is proposed.
Abstract: The ever-increasing energy demand and high penetration rate of distributed renewable generation brings new challenges to the planning of power distribution networks. This paper proposes an expansion planning model for distribution networks by considering multiple types of energy resources in distribution side, including shared electric vehicle (SEV) charging stations, solar-based distributed generation sources, and battery energy storage systems. The operational characteristics of SEV are considered and modeled. The proposed planning model aims to minimize the weighted sum of network investment cost, energy losses, and queue waiting time of SEVs. A stochastic scenario generation method is introduced to address the stochastic feature of SEVs’ driving behaviors. Numerical studies are tested on the systems with 54-node distribution network and 25-node traffic network.
TL;DR: The proposed method (dc-ADMM-P) adopts a novel strategy which uses consensus ADMM to solve the dual of dc-DOPF-CET while only discloses boundary branches information among adjacent subsystems, and shows the improvement of convergence performance by reducing the number of dual multipliers and employing a new update strategy for the multiplier.
Abstract: This article presents a distributed alternating direction method of multipliers (ADMMs) approach for solving the direct current dynamic optimal power flow with carbon emission trading (dc-DOPF-CET) problem. Generally, the ADMM-based distributed approaches disclose boundary buses and branches information among adjacent subsystems. As opposed to these methods, the proposed method (dc-ADMM-P) adopts a novel strategy which uses consensus ADMM to solve the dual of dc-DOPF-CET while only discloses boundary branches information among adjacent subsystems. Moreover, the convergence performance of dc-ADMM-P is improved by reducing the number of dual multipliers and employing an improved update step of the multiplier. DC-ADMM-P is tested on cases ranging from 6 to 1062 buses, with comparison with other distributed/decentralized methods. The simulation results verify the high efficiency of dc-ADMM-P in solving the dc-DOPF problem with complex (nonlinear) factors which can be formulated as convex separable functions. Meanwhile, it also shows the improvement of convergence performance by reducing the number of dual multipliers and employing a new update strategy for the multiplier.
TL;DR: Experimental results show that the proposed hybrid static image forecaster provides superior performance as compared to the benchmarking methods, and case studies at multiple time scales reveal that sky-image-based models can be more robust to the ramp events in solar photovoltaic generation.
TL;DR: A three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint is developed and results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets.
Abstract: Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the grid reconfiguration of the NMGs is more completed and challenging. This article develops a three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint. DLR can potentially affect the ampacity of feeders, especially in the islanding mode, when lines approach their maximum capacity. In order to avoid any feeder contingency in the off-grid (islanded) mode, the reconfiguration technique as well as the cloud fogs framework are employed to change the topology of the NMG network swiftly and release the line capacity. Finally, the proposed problem is formulated as a mixed-integer linear optimization problem considering DLR constraint. The proposed model is examined on a modified IEEE 69 bus test network. Results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets.
TL;DR: This article proposes a fully distributed algorithm to address the EDP over directed networks and takes into account communication delays and noisy gradient observations, which proves that the optimal dispatch can be achieved under the assumptions that the nonidentical constant communication delays inflicting on each link are uniformly bounded.
Abstract: The increased complexity of modern energy network raises the necessity of flexible and reliable methods for smart grid operation. To this end, this article is centered on the economic dispatch problem (EDP) in smart grids, which aims at scheduling generators to meet the total demand at the minimized cost. This article proposes a fully distributed algorithm to address the EDP over directed networks and takes into account communication delays and noisy gradient observations. In particular, the rescaling gradient technique is introduced in the algorithm design and the implementation of the distributed algorithm only resorts to row-stochastic weight matrices, which allows each generator to locally allocate the weights on the messages received from its in-neighbors. It is proved that the optimal dispatch can be achieved under the assumptions that the nonidentical constant communication delays inflicting on each link are uniformly bounded and the noises embroiled in gradient observation of every generator are bounded variance zero mean. Simulations are provided to validate and testify the effectiveness of the presented algorithm.
TL;DR: To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems and its effectiveness is validated in the modified IEEE 123 bus test distribution system.
Abstract: To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an “event-based” model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system.
TL;DR: A novel three-stage stochastic planning model for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency Response stage (ERS) shows the effectiveness and superiority of the proposedThree-stage MEG planning model over the traditional two-stage model.
Abstract: Mobile emergency generators (MEGs) can effectively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience Therefore, MEGs are required to be optimally allocated and utilized For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS) Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads The proposed planning model can be large-scale due to multiple scenarios Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden The simulation results in 13 and 123 node distribution systems show the effectiveness and superiority of the proposed three-stage MEG planning model over the traditional two-stage model
TL;DR: A load-weighted voltage deviation index (LVDI) is proposed to quantify network voltage deviation to obtain robust Pareto solutions under uncertainties and a multi-objective adaptive voltage/VAR control (VVC) framework which coordinates multiple devices in multiple timescales to minimize voltage deviation and power loss simultaneously is proposed.
Abstract: In active distribution networks, high penetration of distributed photovoltaic power generation may cause voltage fluctuation and violation issues. To conquer the challenges, this paper firstly proposes a load-weighted voltage deviation index (LVDI) to quantify network voltage deviation. Secondly, this paper proposes a multi-objective adaptive voltage/VAR control (VVC) framework which coordinates multiple devices in multiple timescales to minimize voltage deviation and power loss simultaneously. Then, a multi-objective adaptive robust optimization method is proposed to obtain robust Pareto solutions under uncertainties. Accordingly, solution algorithms based on different multi-objective programming algorithms and a column-and-constraint generation algorithm are developed and systematically compared. The proposed method is verified through comprehensive tests on the IEEE 123-bus system and simulation results demonstrate high effectiveness of the LVDI, high efficiency of the solution algorithms and full operating robustness of the proposed VVC method against any uncertainty realization.
TL;DR: A multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated on different timescales can guarantee a robustly optimal operation solution for the iM EMG against any uncertainty realization.
Abstract: Manufactory load allocation can be used as an effective industrial demand response scheme to reduce operating costs for industrial multienergy microgrids (iMEMGs). In addition, combined cooling, heat, and power (CCHP) plants with auxiliary devices can provide low-cost multiple energies for industrial plants. However, uncertain power generation from renewable energy sources impairs the iMEMG's operation, leading to challenges such as increased operating costs and energy supply deficiency. To conquer these challenges, this paper proposes a multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated on different timescales. In the weekly scheduling stage, industrial loads and CCHP units are scheduled for the following week and the hourly iMEMG operation is optimized within the week. Besides, this paper applies an adaptive robust optimization method where the uncertain renewable power generation is fully addressed. The proposed approach is tested on an iMEMG with various industrial manufactories, and it is compared with conventional methods. The simulation results indicate that compared to the conventional ones, the proposed approach can guarantee a robustly optimal operation solution for the iMEMG against any uncertainty realization.
TL;DR: A low-carbon electricity network transition model is proposed, helping to plan aging CFPP retirement and renewable power plants installation as well as network augmentation and can achieve carbon mitigation, cost and risk benefits and provide a roadmap to guide the energy network transformation towards a low- carbon economy.
Abstract: In order to achieve sustainable development goals, modern power systems need to achieve a low-carbon transition. Retirement and replacement of aging coal-fired power plants (CFPP) is the major part of electricity network transition. However, the sudden retirement of large aging CFPP could lead to power supply or reserve shortages and generally, it takes several years to complete the replacement process. This paper proposes a low-carbon electricity network transition model, helping to plan aging CFPP retirement and renewable power plants installation as well as network augmentation. The trade-off decisions are found among three conflicting objectives including cost, risk and carbon emission. Moreover, the carbon emission flow (CEF) model is applied in the planning process to calculate carbon emissions from the demand side, in order to assess the efficacy of the low-carbon transition. A relatively new multi-objective natural aggregation algorithm (MONAA) is introduced and applied to find the optimal solution. The proposed model is verified on a modified IEEE 24-bus RTS system and a modified IEEE 118-bus system. According to the numerical results, the proposed model can achieve carbon mitigation, cost and risk benefits and provide a roadmap to guide the energy network transformation towards a low-carbon economy.
TL;DR: An integrated expansion planning framework based on a multiobjective mixed-integer nonlinear program to minimize the net present value of investments considering feeder routing, substation alterations and construction while maximizing the utilization of proposed charging stations is proposed.
TL;DR: The complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association is explained and a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis is proposed for more reliable power plant condition monitoring.
Abstract: Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis for more reliable power plant condition monitoring. It is able to dynamically capture the most significant relationship to develop more reliable normal condition interval, based on which the potential faults can be timely detected and the abnormal variable can be accurately identified. The proposed system was tested on a realistic thermal power plant. The testing results demonstrate its reliable condition monitoring and accurate anomaly detection performance, which necessitates the composite modeling of temporal dependency and cross-variable association in data-driven power plant condition monitoring.
TL;DR: A dual-mode Lyapunov-based EMPC has been formulated to guarantee the stability of the system with disturbance, and under certain conditions, the closed-loop system state can be driven and hold in a safe region.
Abstract: This paper proposed a dual-mode distributed economic model predictive control (EMPC) for a nonlinear wind–photovoltaic–battery microgrid power system (WPB-MPS). The communications between each subsystem are taken into consideration and the economic objectives that are established based on the aspects that: satisfy the total demand; make full use of the power generated by the WPB-MPS; optimize the battery's state of charge; and reduce the fluctuation of power exchange with the grid. The subsystem-based EMPCs work iteratively and cooperatively to solve the economic objective functions, which reflect economies of the system and not necessary to drive the system to a certain steady state as hierarchical control. A dual-mode Lyapunov-based EMPC has been formulated to guarantee the stability of the system with disturbance, and under certain conditions, the closed-loop system state can be driven and hold in a safe region. Simulations demonstrate the advantages and efficiency of the proposed method.