Showing papers by "Zhao Yang Dong published in 2015"
TL;DR: In this article, a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS) is proposed, which can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS.
Abstract: This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.
204 citations
TL;DR: This paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market, and develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection.
Abstract: In the context of energy saving and carbon emission reduction, the electric vehicle (EV) has been identified as a promising alternative to traditional fossil fuel-driven vehicles. Due to a different refueling manner and driving characteristic, the introduction of EVs to the current logistics system can make a significant impact on the vehicle routing and the associated operation costs. Based on the traveling salesman problem, this paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market. The proposed model aims to minimize the total distribution costs of the EV route while satisfying the constraints of battery capacity, charging time and delivery/pickup demands, and the impact of vehicle loading on the unit electricity consumption per mile. To solve the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection. A comprehensive numerical test is conducted on the 36-node and 112-node systems, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm.
186 citations
TL;DR: A mechanism of event-triggering distributed sampling information designed to drive the controller update of each node to reach global synchronization based on algebraic graph, matrix theory and Lyapunov control method is introduced.
176 citations
TL;DR: In this article, a novel expansion co-planning (ECP) framework is proposed to address the challenges of increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner.
Abstract: As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework.
168 citations
TL;DR: In this article, the authors proposed a mixed integer nonlinear programming (MILP) model to solve the problem of co-planning of gas power plants, electricity transmission lines and gas pipelines.
Abstract: Natural gas is an important fuel source in the power industry. Electricity and natural gas are both energy that can be directly consumed. To improve the overall efficiency of the energy infrastructure, it is imperative that the expansion of gas power plants, electricity transmission lines and gas pipelines can be co-planned. The co-planning process is modeled as a mixed integer nonlinear programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of social welfare. To evaluate the robustness of plans under different scenarios, the flexibility criterion is used to identify each plan’s adaptation cost to uncertainties, such as demand growth, fuel cost and financial constraints, etc. We developed a systematic and comprehensive planning model to understand, develop and optimize energy grids in order to reach higher social welfare, and is therefore of great importance in terms of supporting and guiding investment decisions for the power and gas industry. Meanwhile, we use the sequential importance sampling (SIS) to perform scenario reduction for achieving a higher computational efficiency. A comprehensive case study on the integrated IEEE 14-bus and a test gas system is conducted to validate our approach.
141 citations
TL;DR: In this article, a self-adaptive TSA decision-making mechanism is designed to progressively adjust the response time, such that the IS can do the classification faster, thereby allowing more time for emergency controls.
Abstract: Intelligent system (IS) using synchronous phasor measurements for transient stability assessment (TSA) has received continuous interests recently. For post-disturbance TSA, one pivotal concern is the response time, which was reported in the literature as a fixed value ranging from 4 cycles to 3 s after fault clearance. Since transient instability can develop very fast, there is a pressing need for faster response speed. This paper develops a novel IS to balance the response speed and accuracy requirements. A set of classifiers are sequentially organised, each is an ensemble of extreme learning machines (ELMs), whose inputs are post-disturbance generator voltage trajectories and outputs are the classification on the stable/unstable status of the post-disturbance system and an evaluation of the credibility of the classification. A self-adaptive TSA decision-making mechanism is designed to progressively adjust the response time, such that the IS can do the classification faster, thereby allowing more time for emergency controls. The ELM ensemble classifiers can also be updated by on-line pre-disturbance TSA results due to its very fast learning speed. Case studies on the New England system and IEEE 50-machine system have validated the high efficiency and accuracy of the IS.
137 citations
TL;DR: In this paper, the original wind power series is first decomposed and grouped into components of reduced order of complexity using ensemble empirical mode decomposition and sample entropy techniques, and the methods for the prediction of these components with extreme learning machine technique and the formation of the overall optimal PIs are then described.
Abstract: High-quality wind power prediction intervals (PIs) are of utmost importance for system planning and operation. To improve the reliability and sharpness of PIs, this paper proposes a new approach in which the original wind power series is first decomposed and grouped into components of reduced order of complexity using ensemble empirical mode decomposition and sample entropy techniques. The methods for the prediction of these components with extreme learning machine technique and the formation of the overall optimal PIs are then described. The effectiveness of proposed approach is demonstrated by applying it to real wind farms from Australia and National Renewable Energy Laboratory. Compared to the existing methods without wind power series decomposition, the proposed approach is found to be more effective for wind power interval forecasts with higher reliability and sharpness.
82 citations
TL;DR: In this paper, a probabilistic transmission expansion model is proposed for planners to tackle the variability and uncertainty factors associated with grid connected wind farms, both load forecast and wind power output uncertainties are considered in the proposed model.
Abstract: With increasing large-scale wind farms being integrated into the power grids, transmission expansion planning (TEP) increasingly requires more flexibility to account for the intermittency as well as other uncertainty factors involved in the process. In this study, a probabilistic TEP model is proposed for planners to tackle the variability and uncertainty factors associated with grid connected wind farms. Both load forecast and wind power output uncertainties are considered in the proposed model. Other factors considered in the model include the forced outage rates of transmission lines and generators, and the wind speed correlation between wind farms. Moreover, the incentive-based demand response (IBDR) program is introduced as a non-network solution instead of the conventional network expansion approaches. The utilities will pay IBDR providers for their contributions to peak demand reduction. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The hierarchical Bender's decomposition algorithm integrated with Monte Carlo simulation is employed to solve the proposed model. Case studies are given using the Garver's six-bus system and the IEEE-reliability test system to show the effectiveness of the method.
82 citations
TL;DR: In this paper, a multi-objective programming (MOP) model is proposed to simultaneously minimize investment cost, unacceptable transient voltage performance, and proximity to steady-state voltage collapse.
Abstract: Static compensators (STATCOMs) are able to provide rapid and dynamic reactive power support within a power system for voltage stability enhancement. While most of previous research focuses on only an either static or dynamic (short-term) voltage stability criterion, this study proposes a multi-objective programming (MOP) model to simultaneously minimise (i) investment cost, (ii) unacceptable transient voltage performance, and (iii) proximity to steady-state voltage collapse. The model aims to find Pareto optimal solutions for flexible and multi-objective decision-making. To account for multiple contingencies and their probabilities, corresponding risk-based metrics are proposed based on respective voltage stability measures. Given the two different voltage stability criteria, a strategy based on Pareto frontier is designed to identify critical contingencies and candidate buses for STATCOM connection. Finally, to solve the MOP model, an improved decomposition-based multi-objective evolutionary algorithm is developed. The proposed model and algorithm are demonstrated on the New England 39-bus test system, and compared with state-of-the-art solution algorithms.
52 citations
TL;DR: A novel pattern discovery (PD)-based fuzzy classification scheme is proposed for the DSA of a power system and a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point.
Abstract: Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.
48 citations
TL;DR: In this article, a new approach for power system transient stability preventive control is proposed by performing trajectory sensitivity analysis on the one-machine-infinite-bus equivalence of multi-machine systems.
Abstract: A new approach for power system transient stability preventive control is proposed by performing trajectory sensitivity analysis on the one-machine-infinite-bus (OMIB) equivalence of multi-machine systems. Exact instability time/angle are determined from the equivalent OMIB power-angle curve; the trajectory sensitivity is calculated at the instability time and the transient stability of the multi-machine system is controlled by constraining the OMIB's angle excursion at the instability time to that of the critical OMIB which corresponds to the marginally stable condition of the system. The required preventive control action (generation rescheduling) can be efficiently solved via a linear programming model with the OMIB trajectory sensitivities-based constraints. Simulation results on the New England 10-machine 39-bus system and a 285-machine and 1648-bus system validate its effectiveness and superiority over previous trajectory sensitivity applications to this problem.
TL;DR: A distributed model predictive control strategy for battery energy storage systems is proposed to regulate voltage in distribution network with high-renewable penetration, shown to be highly effective through a simulation case study.
Abstract: In this letter, a distributed model predictive control strategy for battery energy storage systems is proposed to regulate voltage in distribution network with high-renewable penetration. Control actions are calculated based on communication between interconnected neighboring subsystems and a multistep receding optimization, also considering system and battery constraints. The proposed approach is shown to be highly effective through a simulation case study, indicating high potential for applications.
TL;DR: Using nonsmooth analysis, the theory of differential inclusions and Lyapunov-like method, the equilibrium point of the proposed neural networks can converge to an optimal solution of optimal real-time price under certain conditions.
TL;DR: In this paper, a tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach is proposed, which is modeled without explicit differential-algebraic equations, reducing the problem size to one very similar to a conventional SCUC.
Abstract: Traditional security-constrained unit commitment (SCUC) considers only static security criteria, which may however not ensure the ability of the system to survive dynamic transition before reaching a viable operating equilibrium following a large disturbance, such as transient stability. This paper proposes a tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach. The problem is modeled without explicit differential-algebraic equations, reducing the problem size to one very similar to a conventional SCUC. The whole problem is decomposed into a master problem for UC and a range of subproblems for steady-state security evaluation and transient stability assessment (TSA). Additional constraints including Benders cut and so-named stabilization cut are generated for eliminating the security/stability violations. The extended equal-area criterion (EEAC) is used for fast TSA and analytically deriving the stabilization cut, wherein multiple contingencies having common instability mode can be simultaneously stabilized by one cut. The proposed approach is demonstrated on the New England 10-machine system and the IEEE 50-machine system, reporting very high computational efficiency and high-quality solutions.
TL;DR: The proposed tight relaxation method is competitive with general-purpose mixed integer programming solvers based methods for large-scale UC problems due to the excellent performance and the good quality of the solutions it generates.
Abstract: This paper presents a novel method to solve the unit commitment (UC) problem by solving a sequence of increasingly tight continuous relaxations based on the techniques of reformulation and lift-and-project (L&P). After projecting the power output of unit onto [0, 1], the continuous relaxation of the UC problem can be tightened with the reformulation techniques. Then, a tighter model which is called L&P-TMIP is established by strengthening the continuous relaxation of the feasible region of the tight UC problem iteratively using L&P. High-quality suboptimal solutions can be obtained from the solutions of the relaxation for this tight model. The simulation results for realistic instances that range in size from 10 to 200 units over a scheduling period of 24 h show that the proposed tight relaxation method is competitive with general-purpose mixed integer programming solvers based methods for large-scale UC problems due to the excellent performance and the good quality of the solutions it generates.
TL;DR: In this paper, an optimal sizing method for mobile battery energy storage system (MBESS) in the distribution system with renewables is proposed, which is formulated as a bi-objective problem, considering the reliability improvement and energy transaction saving.
Abstract: An optimal sizing method is proposed in this paper for mobile battery energy storage system (MBESS) in the distribution system with renewables. The optimization is formulated as a bi-objective problem, considering the reliability improvement and energy transaction saving, simultaneously. To evaluate the reliability of distribution system with MBESS and intermittent generation sources, a new framework is proposed, which is based on zone partition and identification of circuit minimal tie sets. Both analytic and simulation methods for reliability assessment are presented and compared in the framework. Case studies on a modified IEEE benchmark system have verified the performance of the proposed approach.
TL;DR: Simulation results of a practical 10-kV distribution system show that the proposed approach of fault protection is able to achieve better identification accuracy than the hierarchical clustering algorithm and Fuzzy c-means algorithms-based approaches.
Abstract: This paper presents a new approach for single phase-earth fault protection in distribution systems. Traditional protection schemes are analyzed and compared with one another for effective fault feature extraction. The maximum-likelihood method is also carried out on several copula functions to find the optimal copula to fit the fault feature data. Then, copula rank correlation is calculated by the optimal copula, and the principal component analysis technique is improved to preprocess and reduce the dimension of the fault data by decomposing the copula rank correlation matrix instead of computing the eigenvalues and their eigenvectors of the covariance matrix. Finally, the distance discriminant function is defined and operation criterion is proposed, and distance discriminant analysis is used to discriminate the faulty feeder. Simulation results of a practical 10-kV distribution system show that the proposed approach of fault protection is able to achieve better identification accuracy than the hierarchical clustering algorithm and Fuzzy c-means algorithms-based approaches.
TL;DR: In this article, a projected MIQP formulation of thermal unit commitment (UC) is presented, where the power output of a unit is projected onto [0, 1] to form a novel formulation, denoted as P-MIQP.
26 Jul 2015
TL;DR: In this paper, a Mixed Integer Linear Programming (MILP) based rolling optimization approach under real-time pricing (RTP) policy is introduced to efficiently manage energy consumption of a smart home equipped with a battery energy storage system (BESS) and a solar PV system.
Abstract: In this paper, a Mixed Integer Linear Programming (MILP) based rolling optimization approach under real time pricing (RTP) policy is introduced to efficiently manage energy consumption of a smart home equipped with a Battery Energy Storage System (BESS) and a solar PV system. Models of distributed energy resources (DERs) in a smart house are developed and accordingly optimal management of total energy consumption is formulated as a MILP problem, which is then solved by the MOSEK software platform. And a rolling optimization scheduling framework based on MILP is proposed to dispatch DERs within a smart home to minimize the expenditure under RTP policy. Case studies are conducted to validate the performance of the algorithm. It is observed that proposed algorithm could benefit both smart house owners and network operators technically and economically in the context of smart grid.
TL;DR: In this paper, an optimal power dispatch scheduling method based on model predictive control (MPC) scheme is proposed for a wind farm with battery energy storage system, which aims to minimize the energy loss of the wind farm and the battery usage while meeting the grid constraints.
TL;DR: Wang et al. as discussed by the authors comprehensively reviewed the existing achievements of renewable technology development and current status including relevant policies from central government and some on-going demonstration projects, and proposed new energy vehicles (e.g., electric vehicles) as an effective way of reducing carbon emissions.
Abstract: China experienced a rapid economic growth in the past few decades, whereas it has been facing a critical environmental situation. It is palpable that creating a sustainable energy structure in China is urgently required. As reported in China's 12th Five-year (2011–2015) Plan (which is a five-year development plan that outlines the development focus and strategic plan for different sectors in the next five-year period, published by the State Council of the PRC every five years), integrating more renewable energy, especially from solar and wind sources, becomes an essential part of their smart grid development. This article comprehensively reviews the existing achievements of renewable technology development and current status including relevant policies from central government and some on-going demonstration projects. Promoting new energy vehicles (e.g., electric vehicles) as an effective way of reducing carbon emissions is addressed in this article as well. Finally, the difficulties at the current...
26 Jul 2015
TL;DR: Some emerging technologies in multiple domains which can be utilized to develop the big data applications in the smart grid are introduced and some promisingly big data application scenarios in thesmart grid are summarized.
Abstract: Smart grid has become the most complicated interconnected system in human society. With the advanced sensor infrastructure, the smart grid will continuously generate unprecedented data volume. This paper gives an overview discussion of the big data management & analysis in the smart grid. Firstly, this paper identifies the basic requirements of the big data analysis in the smart grid. Secondly, this paper introduces some emerging technologies in multiple domains which can be utilized to develop the big data applications in the smart grid. Thirdly, this paper summarizes some promisingly big data application scenarios in the smart grid.
TL;DR: In this paper, the authors proposed a reliability assessment approach that is applicable for the coupled gas and electricity networks, which is formulated as a mixed integer nonlinear programming problem so as to minimize investments and enhance the reliability of the overall system.
Abstract: Shale gas resources have the potential to significantly contribute to worldwide energy portfolio. A great number shale gas reserves have been identified in many countries. Connections of newly found gas reserves to the existing energy infrastructures are challenging, as many stakeholders and market uncertainties are involved. The proposed co-planning approach is formulated as a mixed integer nonlinear programming problem so as to minimize investments and enhance the reliability of the overall system. We propose a reliability assessment approach that is applicable for the coupled gas and electricity networks. In addition, the IEEE 24-bus RTS and a test gas system are applied to validate the performance of our approach. Based on the simulation results, the novel expansion co-planning approach is a robust and flexible decision tool, which provides network planners with comprehensive information regarding trade-offs between cost and system reliability.
TL;DR: In this article, an optimal load shedding scheme based on backtracking search algorithm (BSA) was proposed to handle the problem of maintaining the stability of an islanded distribution system.
TL;DR: In this article, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems, based on the complementary features between experimental and agent-based computational methods, and a human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed.
Abstract: The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market design, it is important for policy makers to understand the participants' trading behaviors in different market environments. However, human behaviors cannot be easily modeled with conventional analytical methods due to its “bounded rationality” characteristics. In this paper, based on the complementary features between experimental and agent-based computational methods, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems. Human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed. Major driving factors of emission trading are categorized into emission price, emission quantity and time related factors, which are in accordance with empirical analysis results on EU ETS 2005–2006 transaction dataset. Furthermore, more human-subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis. Based on thousands of samples obtained, the joint influences of driving factors on emission trading behaviors are analyzed. The quantitative analysis results obtained can reflect the trading patterns of human participants, which provide basis for constructing computer agents that can act as useful substitutes for human participants.
TL;DR: In this paper, a probabilistic TEP approach with the integration of a chance constrained load curtailment index is proposed, where the formulated dynamic programming problem is solved by a hybrid solution algorithm in an iterative process.
Abstract: Following the deregulation of the power industry, transmission expansion planning (TEP) has become more complicated due to the presence of uncertainties and conflicting objectives in a market environment. Also, the growing concern on global warming highlights the importance of considering carbon pricing policies during TEP. In this paper, a probabilistic TEP approach is proposed with the integration of a chance constrained load curtailment index. The formulated dynamic programming problem is solved by a hybrid solution algorithm in an iterative process. The performance of our approach is demonstrated by case studies on a modified IEEE 14-bus system. Simulation results prove that our approach can provide network planners with comprehensive information regarding effects of uncertainties on TEP schemes, allowing them to adjust planning strategies based on their risk aversion levels or financial constraints.
26 Jul 2015
TL;DR: In this article, a Mixed Integer Linear Programming (MILP) framework is proposed to accommodate more Building Integrated Photovoltaic (BIPV) System in distribution networks in order to efficiently manage energy consumption of building equipped with battery energy storage system (BESS) and BIPV system.
Abstract: This paper proposes a Mixed Integer Linear Programming (MILP) framework to accommodate more Building Integrated Photovoltaic (BIPV) System in distribution networks. The proposed framework is introduced to efficiently manage energy consumption of building equipped with a Battery Energy Storage System (BESS) and BIPV system. Models of distributed energy resources (DERs) in a building are investigated. The MILP algorithm is solved by an academically-free software platform- MOSEK. Case studies are conducted to validate the performance of the algorithm. It is observed that proposed algorithm could efficiently accommodate BIPV in distribution networks in the context of smart grid.
TL;DR: In this paper, a risk assessment index for wind power prediction is proposed for wpp errors and its calculation, which is defined as an integration of the probability of a power disturbance event caused by wpp error and the losses caused by the event having a monetary dimension.
Abstract: the requirements on the error evaluation index for wind power prediction(wpp)and drawbacks of the traditional evaluation indexes are summarizedby distinguishing the influences on electric power reliability of a positive error from those of a negative error,a risk assessment index is proposed for wpp errors and its calculationthe risk assessment index is defined as an integration of the probability of a power disturbance event caused by wpp error and the losses caused by the eventhaving a monetary dimension,the index can be accumulated as risk cost of wpp error with other costs directlythus,the confusion on how to trade off "ignoring the events of small probability "against' attaching importance to the events with heavy losses" can be dispelled for those events with big error as well as very small probabilityfinally,an actual wind farm system in ningxia,china is applied to illustrating the feasibility and availability of the risk index
TL;DR: A flexible framework of power flow estimation is proposed, where generalized line outage distribution factors and ac power flow model are integrated together to formulate a two-stage scheme, which can provide significantly enhanced accuracy as well as satisfied efficiency.
01 Jan 2015
TL;DR: In this article, a day-ahead DSM optimization problem is formulated, which includes demand, generation, storage, and cost models, and a scenario based study for Australian residential households is conducted to reveal the effect of the pricing and regulation on the DSM's performance.
Abstract: With the advent of demand side management (DSM) in smart grid environment, energy can be operated more effectively from the consumers' side. In this paper firstly a day-ahead DSM optimization problem is formulated, which includes demand, generation, storage and cost models. Then a scenario based study for Australian residential households is conducted to reveal the effect of the pricing and regulation on the DSM's performance. Three typical scenarios of pricing and regulation suitable for Australia are studied, i.e. “Real time pricing scheduling”, “ToU with FiT”, “Real time pricing plus FiT”. A distributed algorithm is used to optimize DSM problem, which can preserve the user's privacy and is also scalable in both time domain and sample size. Through this study, we show the differences and insights of the impact of pricing and regulations on DSM's performance, which can provide useful information for utilities to design proper schemes in the future.