Showing papers in "Journal of Modern Power Systems and Clean Energy in 2018"
TL;DR: In this article, a comprehensive survey on frequency regulation methods for variable speed wind turbines is presented, including the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends.
Abstract: With an increasing penetration of wind power in the modern electrical grid, the increasing replacement of large conventional synchronous generators by wind power plants will potentially result in deteriorated frequency regulation performance due to the reduced system inertia and primary frequency response. A series of challenging issues arise from the aspects of power system planning, operation, control and protection. Therefore, it is valuable to develop variable speed wind turbines (VSWTs) equipped with frequency regulation capabilities that allow them to effectively participate in addressing severe frequency contingencies. This paper provides a comprehensive survey on frequency regulation methods for VSWTs. It fully describes the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends. It concludes with a performance comparison of frequency regulation by the four main types of wind power plants.
161 citations
TL;DR: A Blockchain based smart grid cyber-physical infrastructure model is proposed and some promising application domains of Blockchain in future grids are presented and some potential challenges are discussed.
Abstract: Modern power systems are rapidly evolving into complex cyber-physical systems. The increasingly complex interaction among different energy entities calls for a secure, efficient, and robust cyber infrastructure. As an emerging distributed computing technology, Blockchain provides a secure environment to support such interactions. This paper gives a prospective on using Blockchain as a secure, distributed cyber infrastructure for the future grid. Firstly, the basic principles of Blockchain and its state-of-the-art are introduced. Then, a Blockchain based smart grid cyber-physical infrastructure model is proposed. Afterwards, some promising application domains of Blockchain in future grids are presented. Following this, some potential challenges are discussed.
140 citations
TL;DR: A short history of the phasor measurement unit (PMU) concept is provided, and a number of applications of this technology are discussed, and an account of WAMS activities in many countries around the world are provided.
Abstract: The paper provides a short history of the phasor measurement unit (PMU) concept. The origin of PMU is traced to the work on developing computer based distance relay using symmetrical component theory. PMUs evolved from a portion of this relay architecture. The need for synchronization using global positioning system (GPS) is discussed, and the wide area measurement system (WAMS) utilizing PMU signals is described. A number of applications of this technology are discussed, and an account of WAMS activities in many countries around the world are provided.
135 citations
TL;DR: A model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014.
Abstract: Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Finally, a general anomaly detection framework is proposed for the future research.
72 citations
TL;DR: In this article, an integrated demand response (IDR) scheme is designed to coordinate the operation of P2G devices, heat pumps, diversified storage devices and flexible loads within an extended modeling framework of energy hubs.
Abstract: In recent years, the increasing penetration level of renewable generation and combined heat and power (CHP) technology in power systems is leading to significant changes in energy production and consumption patterns. As a result, the integrated planning and optimal operation of a multi-carrier energy (MCE) system have aroused widespread concern for reasonable utilization of multiple energy resources and efficient accommodation of renewable energy sources. In this context, an integrated demand response (IDR) scheme is designed to coordinate the operation of power to gas (P2G) devices, heat pumps, diversified storage devices and flexible loads within an extended modeling framework of energy hubs. Subsequently, the optimal dispatch of interconnected electricity, natural gas and heat systems is implemented considering the interactions among multiple energy carriers by utilizing the bi-level optimization method. Finally, the proposed method is demonstrated with a 4-bus multi-energy system and a larger test case comprised of a revised IEEE 118-bus power system and a 20-bus Belgian natural gas system.
71 citations
TL;DR: The basic concept and characteristics of the Energy Internet are summarized, and its basic structural framework is analyzed in detail to discuss the operation and planning of integrated energy systems in both deterministic and uncertain environments.
Abstract: With the intensifying energy crisis and environmental pollution, the Energy Internet and corresponding patterns of energy use have been attracting more and more attention. In this paper, the basic concept and characteristics of the Energy Internet are summarized, and its basic structural framework is analyzed in detail. On this basis, couplings between the electric power system and other systems such as the cooling and heating system, the natural gas system, and the traffic system are analyzed, and the operation and planning of integrated energy systems in both deterministic and uncertain environments are comprehensively reviewed. Finally, the research prospects and main technical challenges of the Energy Internet are discussed.
66 citations
TL;DR: In this article, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal, and electrical load, as well as photovoltaic (PV) output power.
Abstract: Energy management is facing new challenges due to the increasing supply and demand uncertainties, which is caused by the integration of variable generation resources, inaccurate load forecasts and non-linear efficiency curves. To meet these challenges, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal and electrical load, as well as photovoltaic (PV) output power. Case study results demonstrate that the robust optimization model performs better than the deterministic optimization model in terms of the expected operation cost. The fluctuation of net electrical load has greater effect on the dispatching results of the combined cooling, heating and power (CCHP) microgrid than the fluctuation of the cooling and thermal load. The day-ahead schedule is greatly affected by the uncertainty budget of the load demand. The economy of the optimal decision could be achieved by adjusting different uncertainty budget levels according to control the conservatism of the model.
65 citations
TL;DR: The future protection research directions lie in the development of novel protection devices, which are based on electronic technology to provide loose protection constraints and the improvement of suitable protection schemes.
Abstract: The DC microgrid has become a typical distribution network due to its excellent performance. However, a well-designed protection scheme still remains a challenge for DC microgrids. At present, researches on DC microgrids primarily focus on the topology structure, control method and energy control, while researches on fault analysis, detection and isolation have not drawn enough attention. Therefore, this paper intends to depict the current research status in different relative areas and review the proposed protection strategies in order to help researchers to have a clear understanding on DC microgrid protection. Meanwhile, to solve the protection issues and promote the development of the DC microgrid, this paper points out the key areas of future research. The future protection research directions lie in the development of novel protection devices, which are based on electronic technology to provide loose protection constraints and the improvement of suitable protection schemes. In addition, the novel concept of coordinated strategy of control and protection of the DC microgrids is explained.
57 citations
TL;DR: If the game between an electric power company and a natural gas company reaches market equilibrium, not only can both companies maximize their profits, but also the coordinated operation of the coupling units, i.e., gas turbines and P2G facilities, will contribute more to renewable energy utilization and carbon emission reduction.
Abstract: As power to gas (P2G) technology gradually matures, the coupling between electricity networks and natural gas networks should ideally evolve synergistically. With the intent of characterizing market behaviors of integrated electric power and natural gas networks (IPGNs) with P2G facilities, this paper establishes a steady-state model of P2G and constructs optimal dispatch models of an electricity network and a natural gas network separately. In addition, a concept of slack energy flow (SEF) is proposed as a tool for coordinated optimal dispatch between the two networks. To study how the market pricing mechanism affects coordinated optimal dispatch in an IPGN, a market equilibrium-solving model for an IPGN is constructed according to game theory, with a solution based on the Nikaido-Isoda function. Case studies are conducted on a joint model that combines the modified IEEE 118-node electricity network and the Belgian 20-node gas network. The results show that if the game between an electric power company and a natural gas company reaches market equilibrium, not only can both companies maximize their profits, but also the coordinated operation of the coupling units, i.e., gas turbines and P2G facilities, will contribute more to renewable energy utilization and carbon emission reduction.
56 citations
TL;DR: Results indicate that the planning model gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.
Abstract: A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system (ESS) in active distribution systems (ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid (MG) and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations (REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution (DE) and particle swarm optimization (PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planning model gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.
54 citations
TL;DR: In this article, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States, including hourly solar radiation, temperature, and wind speed.
Abstract: Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with 16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scores that are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between $$6\%$$
and $$80\%$$
in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation, temperature, and wind speed.
TL;DR: The proposed self-tuning (ST)-VSG, the frequency droop method, and the CP-VSG are evaluated by comparing their effects on attenuating frequency variations under load variations and the ST was found to be more efficient than the other two methods in improving frequency stability.
Abstract: This paper investigates the use of a virtual synchronous generator (VSG) to improve frequency stability in an autonomous photovoltaic-diesel microgrid with energy storage. VSG control is designed to emulate inertial response and damping power via power injection from/to the energy storage system. The effect of a VSG with constant parameters (CP-VSG) on the system frequency is analyzed. Based on the case study, self-tuning algorithms are used to search for optimal parameters during the operation of the VSG in order to minimize the amplitude and rate of change of the frequency variations. The performances of the proposed self-tuning (ST)-VSG, the frequency droop method, and the CP-VSG are evaluated by comparing their effects on attenuating frequency variations under load variations. For both simulated and experimental cases, the ST-VSG was found to be more efficient than the other two methods in improving frequency stability.
TL;DR: A hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution is proposed and it turns out that the proposed method overrides commonly used benchmark models in the case study.
Abstract: Compared to traditional point load forecasting, probabilistic load forecasting (PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperature forecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.
TL;DR: In this paper, a PV yield prediction system is presented based on an irradiance forecast model and a PV model using multiple feed-forward neural networks, which has a mean absolute percentage error of 3.4% on sunny day and 23% on a cloudy day for Stuttgart.
Abstract: In order to develop predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems, accurate and reliable photovoltaic (PV) power forecasts are required. A PV yield prediction system is presented based on an irradiance forecast model and a PV model. The PV power forecast is obtained from the irradiance forecast using the PV model. The proposed irradiance forecast model is based on multiple feed-forward neural networks. The global horizontal irradiance forecast has a mean absolute percentage error of 3.4% on a sunny day and 23% on a cloudy day for Stuttgart. PV power forecasts based on the neural network irradiance forecast have performed much better than the PV power persistence forecast model.
TL;DR: A cyber-secure decentralized energy management framework that applies a distributed decision-making intelligence to networked microgrids while securing their individual mandates for optimal operation is proposed.
Abstract: This paper provides a strategic solution for enhancing the cybersecurity of power distribution system operations when information and operation technologies converge in active distribution network (ADN). The paper first investigates the significance of Internet of Things (IoT) in enabling fine-grained observability and controllability of ADN in networked microgrids. Given severe cybersecurity vulnerabilities embedded in conventionally centralized energy management schemes, the paper then proposes a cyber-secure decentralized energy management framework that applies a distributed decision-making intelligence to networked microgrids while securing their individual mandates for optimal operation. In particular, the proposed framework takes advantage of software-defined networking technologies that can secure communications among IoT devices in individual microgrids, and exploits potentials for introducing blockchain technologies that can preserve the integrity of communications among networked microgrids in ADN. Furthermore, the paper presents the details of application scenarios where the proposed framework is employed to secure peer-to-peer transactive energy management based on a set of interoperable blockchains. It is finally concluded that the proposed framework can play a significant role in enhancing the efficiency, reliability, resilience, and sustainability of electricity services in ADN.
TL;DR: Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads and the impact of the V2G management strategy on the system loading at night is analyzed by implementing an off-line charging scheduling algorithm.
Abstract: This study focuses on the potential role of plug-in electric vehicles (PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid (V2G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2G process is first formulated as an optimal control problem. Then, a two-stage V2G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm.
TL;DR: The vulnerability indices are introduced, which return the most vulnerable component in the system based on a tri-level defender-attacker-operator (DAO) interdiction problem which solves iteratively.
Abstract: The significance of modern power grids is acknowledged every time there is a major threat. This paper proposes the novel approaches to aid power system planner to improve power grid resilience by making appropriate hardening strategies against man-made attack or natural hazards. The vulnerability indices are introduced, which return the most vulnerable component in the system based on a tri-level defender-attacker-operator (DAO) interdiction problem which solves iteratively. The output of DAO is the set of hardening strategies that optimally allocated along the network to mitigate the impact of the worst-case damages. By repeating DAO problem based on the proposed algorithm, the various crafted attack is imposed on the system, and the defender’s behavior demonstrates how an element is vulnerable to threats. The WSCC 9-bus, IEEE 24-bus, and IEEE 118-bus systems are employed to evaluate the model performance. The counter-intuitive results are proven by the proposed robust hardening strategy, which shows how the hardening strategy should be allocated to improve power network resilience against threats.
TL;DR: General recommendations for the treatment of public holidays in energy forecasting are given to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated.
Abstract: We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set, treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%.
TL;DR: This paper presents a model of cascading failures in cyber-physical power systems (CPPSs) based on an improved percolation theory, and then proposes failure mitigation strategies, including strategies to convert some cyber layer nodes into autonomous nodes and embed unified power flow controller into the physical layer.
Abstract: This paper presents a model of cascading failures in cyber-physical power systems (CPPSs) based on an improved percolation theory, and then proposes failure mitigation strategies. In this model, the dynamic development of cascading failures is divided into several iteration stages. The power flow in the power grid, along with the data transmission and delay in the cyber layer, is considered in the improved percolation theory. The interaction mechanism between two layers is interpreted as the observability and controllability analysis and data update analysis influencing the node state transformation and security command execution. The resilience indices of the failures reflect the influence of cascading failures on both topological integrity and operational state. The efficacy of the proposed mitigation strategies is validated, including strategies to convert some cyber layer nodes into autonomous nodes and embed unified power flow controller (UPFC) into the physical layer. The results obtained from simulations of cascading failures in a CPPS with increasing initial failure sizes are compared for various scenarios. Dynamic cascading failures can be separated into rapid and slow processes. The interdependencies and gap between the observable and controllable parts of the physical layer with the actual physical network are two fundamental reasons for first-order transition failures. Due to the complexity of the coupled topological and operational relations between the two layers, mitigation strategies should be simultaneously applied in both layers.
TL;DR: Wang et al. as mentioned in this paper presented a method of heat and power load dispatching by exploring the energy storage ability of electric heating boilers and district heating systems, which can integrate additional wind power into the grid and reduce the coal consumption of CHP units over the optimized period.
Abstract: As one of promising clean and low-emission energy, wind power is being rapidly developed in China. However, it faces serious problem of wind curtailment, particularly in northeast China, where combined heat and power (CHP) units cover a large proportion of the district heat supply. Due to the inherent strong coupling between the power and the heat load, the operational flexibility of CHP units is severely restricted in winter to meet the heat supply demand, which imparts considerable stress on the wind power connection to the grid. To promote the integration of wind power and enhance the flexibility of CHP units, this paper presented a method of heat and power load dispatching by exploring the energy storage ability of electric heating boilers and district heating systems. The optimization results indicate that the proposed method can integrate additional wind power into the grid and reduce the coal consumption of CHP units over the optimized period. Furthermore, the thermal inertia of a district heating system is found to contribute more to the reduction of coal consumption, whereas the electric heating boilers contribute to lower wind curtailment.
TL;DR: From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained.
Abstract: In this paper, a data-driven linear clustering (DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling Then optimal autoregressive integrated moving average (ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained
TL;DR: The theoretical background of PV devices, an overview of MPPT controllers and common mode leakage current, and a detailed investigation of different inverter topologies regarding the ground leakage current are given.
Abstract: This paper gives an overview of previous studies on photovoltaic (PV) devices, grid-connected PV inverters, control systems, maximum power point tracking (MPPT) control strategies, switching devices and transformer-less inverters. The literature is classified based on types of PV systems, DC/DC boost converters and DC/AC inverters, and types of controllers that control the circuit to ensure maximum power tracking and stabilization of load and input voltage. This is followed by the theoretical background of PV devices, an overview of MPPT controllers and common mode leakage current, and a detailed investigation of different inverter topologies regarding the ground leakage current. Furthermore, design principles of power converters, such as DC/DC boost converters, and single-phase inverters are discussed. The paper also discusses limitations and benefits in addition to the basic operating principles of several topologies. Finally, the proposed system is derived and its simulation results are discussed to offer the next generation of grid connected PV systems.
TL;DR: This paper proposes using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks.
Abstract: It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.
TL;DR: A three-stage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks.
Abstract: The integration of natural gas in electricity network requires a more reliable operating plan for increasing uncertainties in the whole system. In this paper, a three-stage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks. Non-convex constrains are linearized in order to formulate the dual problem of optimal energy flow. Then, the proposed three-stage problem can be reformulated into a two-stage mixed integer linear program (MILP) and solved by Benders decomposition algorithm. Numerical studies on IEEE 30-bus power system with 7-node natural gas network and IEEE 118-bus power system with 14-node natural gas network validate the feasibility of the proposed model for improving resilience of integrated energy system. Energy storage facilities are also considered for the resiliency analysis.
TL;DR: In this paper, the essential mechanisms of LCL-resonance damping and reviews state-of-the-art resonance damping strategies are classified into those with single-state and multi-state feedback.
Abstract: Grid-connected LCL-filtered inverters are commonly used for distributed power generators. The LCL resonance should be treated properly. Recently, many strategies have been used to damp the resonance, but the relationships between different damping strategies have not been thoroughly investigated. Thus, this study analyses the essential mechanisms of LCL-resonance damping and reviews state-of-the-art resonance damping strategies. Existing resonance damping strategies are classified into those with single-state and multi-state feedback. Single-state feedback strategies damp the LCL resonance using feedback of a voltage or current state at the resonance frequency. Multi-state feedback strategies are summarized as zero-placement and pole-placement strategies, where the zero-placement strategy configures the zeros of a novel state combined by multi-state feedback, while the pole-placement strategy aims to assign the closed-loop poles freely. Based on these mechanisms, an investigation of single-state and multi-state feedback is presented, including detailed comparisons of the existing strategies. Finally, some future research directions that can improve LCL-filtered inverter performance and minimize their implementation costs are summarized.
TL;DR: In this paper, the impact of human factors on power system reliability is analyzed. But, there is no comprehensive study on human factors and human reliability analysis in power systems, and no comprehensive analysis methods are proposed according to specified situations, and these methods are verified by some power system practical cases.
Abstract: Along with the improvement of electrical equipment reliability, people’s unsafe behaviors and human errors have become one of main sources of risks in power systems. However, there is no comprehensive study on human factors and human reliability analysis in power systems. In allusion to this situation, this paper attempts to analyze the impact of human factors on power system reliability. First, this paper introduces current situation of human factors in power systems and the latest research progress in this field. Several analysis methods are proposed according to specified situations, and these methods are verified by some power system practical cases. On this base, this paper illustrates how human factors affect power system operation reliability from 2 typical aspects: imperfect maintenance caused by human errors, and impact of human factors on emergency dispatch operation and power system cascading failure. Finally, based on information decision and action in crew (IDAC), a novel dispatcher training evaluation simulation system (DTESS) is established, which can incorporate all influencing factors. Once fully developed, DTESS can be used to simulate dispatchers’ response when encountering an initial event, and improve power system dispatching reliability.
TL;DR: In this paper, a hybrid nonlinear regression and support vector machine (SVM) model is proposed to forecast the prices of peak hours in peak months and off-peak hours in off peak months.
Abstract: With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine (SVM) model is proposed. Off-peak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.
TL;DR: A method of optimal allocation of synchronous condensers in a hypothetic future scenario of a transmission system fed by renewable generation is proposed, which has potential for application on larger grids, aiding grid-integration of RES.
Abstract: Modern power systems, employing an increasing number of converter-based renewable energy sources (RES) and decreasing the usage of conventional power plants, are leading to lower levels of short-circuit power and rotational inertia. A solution to this is the employment of synchronous condensers in the grid, in order to provide sufficient short-circuit power. This results in the increase of the short-circuit ratio (SCR) at transmission system bus-bars serving as points of interconnection (POI) to renewable generation. Evaluation of the required capacity and grid-location of the synchronous condensers, is inherently a mixed integer nonlinear optimization problem, which could not be done on manual basis considering each type of machine and all bus-bars. This study therefore proposes a method of optimal allocation of synchronous condensers in a hypothetic future scenario of a transmission system fed by renewable generation. Total cost of synchronous condenser installations in the system is minimized and the SCRs at the POIs of central renewable power plants are strengthened. The method has potential for application on larger grids, aiding grid-integration of RES.
TL;DR: An integrated resources planning model considering the impact of interruptible loads and shiftable loads in microgrids, which simultaneously deals with supply side and demand side resources and minimizes the overall planning cost of the microgrid is presented.
Abstract: Demand response has the potential to bring significant benefits to the optimal sizing of distributed generation (DG) resources for microgrids planning. This paper presents an integrated resources planning model considering the impact of interruptible loads (IL) and shiftable loads (SL) in microgrids, which simultaneously deals with supply side and demand side resources and minimizes the overall planning cost of the microgrid. The proposed model can be applied to offer a quantitative assessment how IL and SL can contribute to microgrid planning. The pure peak clipping model with IL and SL is also provided for comparisons. Moreover, sensitivity analysis of parameters in the model is performed. Numerical results confirm that the proposed model is an effective method for reducing the planning cost of microgrids. It was also found that the major contributing factors of IL and SL have great impact on the economic benefits of the proposed model in low-carbon economy environments.
TL;DR: A hybrid interval analytic hierarchy process (IAHP) and interval entropy method is proposed for electricity user evaluation (EUE), which combines subjective and objective information to derive the optimal combined index weights.
Abstract: Smart electricity utilization (SEU) is one of the most important components in a smart grid. It is crucial to evaluate efficiency, safety, and demand response capability of electricity users to achieve the smart use of electricity. The analytic hierarchy process (AHP) uses subjective criteria to determine index weights in multi-criteria decision-making problems, while the entropy method provides objectivity in determining index weights. Taking into account the uncertainty of expert scoring and user data, a hybrid interval analytic hierarchy process (IAHP) and interval entropy (IE) method is proposed for electricity user evaluation (EUE). Specifically, in the proposed method, electricity users are evaluated in terms of energy efficiency, safety monitoring, and demand response. The weights of EUE indices are calculated under uncertainty. The proposed approach derives subjective weights of EUE indices by the IAHP with expert scoring as input data, and determines objective weights of EUE indices by the IE method with user data as inputs. In order to obtain the optimal combined index weights, the two weights are normalized by a selected weight factor. Numerical case studies illustrate the effectiveness and advantages of the proposed approach, which combines subjective and objective information to derive the optimal combined index weights.