Abstract: Abstract Distributed generations (DGs) are main components for active distribution networks (ADNs). Owing to the large number of DGs integrated into distribution levels, it will be essential to schedule active and reactive power resources in ADNs. Generally, energy and reactive power scheduling problems are separately managed in ADNs. However, the separate scheduling cannot attain a global optimum scheme in the operation of ADNs. In this paper, a probabilistic simultaneous active/reactive scheduling framework is presented for ADNs. In order to handle the uncertainties of power generations of renewable-based DGs and upstream grid prices in an efficient framework, a stochastic programming technique is proposed. The stochastic programming can help distribution system operators (DSOs) to make operation decisions in front of existing uncertainties. The proposed coordinated model considers the minimization of the energy and reactive power costs of all distributed resources along with the upstream grid. Meanwhile, a new payment index as loss profit value for DG units is introduced and embedded in the model. Numerical results based on the 22-bus and IEEE 33-bus ADNs validate the effectiveness of the proposed method. The obtained results verify that through the proposed stochastic-based power management system, the DSO can effectively schedule all DGs along with its economic targets while considering severe uncertainties. PubDate: 2019-11-01
Abstract: Abstract With the construction of the “3-level, 3-vertical-line, and 1-circle” power backbone in China, it’s stricter and stricter on relay protection system and security and stability control system (SSCS) for reliable power transmission. Lots of blackouts in the world had happened, one main reason for which is the hidden failures of relay protection system or SSCS. Much work had been done about the hidden failure of relay protection, including classification, probability model, analysis methods of effects on power grid, and monitoring measures, which was summarized in the paper. The operation experiences of SSCS indicated that there might be hidden failures in five links of the security and stability control device (SSCD), e.g. measuring, control strategy, setting, communication and voting pattern. In addition, the coordination hidden failure among relay protection system, SSCS, and power plant’s parameters related to the power grid was pointed out for more attention. In the future, amounts of work will be expected to be conducted on hidden failure: model building, assessment methods, application of research achievements, operation management of secondary equipment, and coordination problem between the relay protection system and the SSCS. PubDate: 2019-11-01
Abstract: Abstract Due to the growing penetration of renewable energies (REs) in integrated energy system (IES), it is imperative to assess and reduce the negative impacts caused by the uncertain REs. In this paper, an unscented transformation-based mean-standard (UT-MS) deviation model is proposed for the stochastic optimization of cost-risk for IES operation considering wind and solar power correlated. The unscented transformation (UT) sampling method is adopted to characterize the uncertainties of wind and solar power considering the correlated relationship between them. Based on the UT, a mean-standard (MS) deviation model is formulated to depict the trade-off between the cost and risk of stochastic optimization for the IES optimal operation problem. Then the UT-MS model is tackled by a multi-objective group search optimizer with adaptive covariance and Lévy flights embedded with a multiple constraints handling technique (MGSO-ACL-CHT) to ensure the feasibility of Perato-optimal solutions. Furthermore, a decision making method, improve entropy weight (IEW), is developed to select a final operation point from the set of Perato-optimal solutions. In order to verify the feasibility and efficiency of the proposed UT-MS model in dealing with the uncertainties of correlative wind and solar power, simulation studies are conducted on a test IES. Simulation results show that the UT-MS model is capable of handling the uncertainties of correlative wind and solar power within much less samples and less computational burden. Moreover, the MGSO-ACL-CHT and IEW are also demonstrated to be effective in solving the multi-objective UT-MS model of the IES optimal operation problem. PubDate: 2019-11-01
Abstract: Abstract With the large-scale integration of renewable generation, energy storage system (ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty. This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load. A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS. Specifically, the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making. Then, an easy-to-implement variant of Benders decomposition (BD) algorithm is developed to solve the resulting mixed integer nonlinear programming problem. Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power. In addition, the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems. PubDate: 2019-11-01
Abstract: Abstract A hierarchical approach for the energy management of geographically close microgrids connected through a dedicated AC power network is proposed in this paper. The proposed approach consists of a two-layer energy management system (EMS) for networked microgrids. In the lower layer, each microgrid solves its own economic dispatch problem through a distributed model predictive control approach that respects capacity limits and ramp-rate constraints of distributed generation. In the upper layer, the energy trading in the network of microgrids decides how to optimally trade the energy based on the marginal cost information from the lower layer in order to improve global optimization objectives, e.g., social welfare. In order to solve the trading problem, a consensus-based algorithm and a replicator dynamics algorithm are proposed assuming that the marginal cost function of the microgrid is known and linear. It is shown that both algorithms converge to the same solution, which is equivalent to the minimization of operation costs. The consensus-based algorithm is extended in order to tackle more general marginal cost functions and trading network constraints. Moreover, the effect of ramp constraints and network limits is studied. Simulations show the effectiveness of the proposed algorithms for three interconnected microgrids with different characteristics. PubDate: 2019-11-01
Abstract: Abstract The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems. The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems. Therefore, it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration. In this paper, a novel reliability evaluation method for integrated electricity–gas systems (IEGSs) is proposed. First, reliability network equivalents are utilized to represent reliability models of gas-fired generating units, gas sources (GSs), power-to-gas facilities, and other conventional generating units in IEGS. A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique. Finally, the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents. The proposed method is capable to evaluate customers’ reliabilities in IEGS, which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system. PubDate: 2019-11-01
Abstract: Abstract Fast and accurate forecasting of schedulable capacity of electric vehicles (EVs) plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems. Traditional methods are insufficient to deal with large-scale actual schedulable capacity data. This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales. The time scale of these data analysis comprises the real time of one minute, ultra-short-term of one hour and one-day-ahead scale of 24 hours. The predicted results for different time scales can be used for various ancillary services. The proposed algorithm is validated using operation data of 521 EVs in the field. The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm, the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment. PubDate: 2019-11-01
Abstract: Abstract This paper proposes a robust controller to improve power system stability and mitigate subsynchronous interaction (SSI) between doubly-fed induction generator (DFIG)-based wind farms and series compensated transmission lines. A robust stability analysis is first carried out to show the impact of uncertainties on the SSI phenomenon. The uncertainties are mainly due to the changes in the power system impedance (e.g., transmission line outages) and the variations of wind farm operating conditions. Then, using the µ-synthesis technique, a robust SSI damping controller is designed and augmented to the DFIG control system to effectively damp the SSI oscillations. The output signals of the supplementary controller are dynamically limited to avoid saturating the converters and to provide DFIG with the desired fault-ride-through (FRT) operation during power system faults. The proposed controller is designed for a realistic test system with multiple series capacitor compensated lines. The frequency of the unstable SSI mode varies over a wide range due to the changes in power system topologies and wind farm operating conditions. The performance of the proposed controller is verified through electromagnetic transient (EMT) simulations using a detailed wind farm model. Simulation results also confirm the grid compliant operation of the DFIG. PubDate: 2019-11-01
Abstract: Abstract In power market environment, the growing importance of demand response (DR) and renewable energy source (RES) attracts more for-profit DR and RES aggregators to compete with each other to maximize their profit. Meanwhile, the intermittent natures of these alternative sources along with the competition add to the probable financial risk of the aggregators. The objective of the paper is to highlight this financial risk of aggregators in such uncertain environment while estimating DR magnitude and power generated by RES. This work develops DR modeling incorporating the effect of estimating power at different confidence levels and uncertain participation of customers. In this paper, two well-known risk assessment techniques, value at risk and conditional value at risk, are applied to predict the power from RES and DR programs at a particular level of risk in different scenarios generated by Monte Carlo method. To establish the linkage between financial risk taking ability of individuals, the aggregators are classified into risk neutral aggregator, risk averse aggregator and risk taking aggregator. The paper uses data from Indian Energy Exchange to produce realistic results and refers certain policies of Indian Energy Exchange to frame mathematical expressions for benefit function considering uncertainties for each type of three aggregators. Extensive results show the importance of assessing the risks involved with two unpredictable variables and possible impacts on technical and financial attributes of the microgrid energy market. PubDate: 2019-11-01
Abstract: Abstract To explore the clustered voltage balancing mechanism of the cascaded H-bridge static synchronous compensator (STATCOM), this paper analyzes the causes of unbalanced clustered voltage. The negative-sequence current caused by the compensation of unbalanced reactive power or detection and control errors and the zero-sequence voltage caused by voltage drift of the STATCOM neutral point contribute to unbalanced clustered voltage. On this basis, this paper proposes a control strategy to inject negative-sequence current and zero-sequence voltage simultaneously. The injection of negative-sequence current may cause current asymmetry in the grid, and the zero-sequence injection has a relatively limited balancing ability in the clustered voltages. The proposed control strategy can not only generate a faster balancing response than the traditional zero-sequence voltage injection method, but also lower the extent of current asymmetry compared with the traditional negative-sequence current injection method. Then, the negative-sequence current and zero-sequence voltage injection are further transformed into the dq frame to establish a unified frame. The effectiveness of the proposed control strategy is verified by the simulation and experimental results. PubDate: 2019-11-01
Abstract: Abstract Heat storage systems with multiple heat sources play an important role in consuming extra wind power. A reasonable scheduling strategy for a hybrid system with multiple heat and electric sources could provide greater economic benefits. However, the present scheduling methods primarily focus on extra wind power consumption alone. This paper aims to develop a coordinated dispatching method that targets the maximum extra wind power consumed and highest economic benefit of the hybrid energy system as the optimization objective. A two-step coordinated dispatching method is proposed, where the first step focuses on optimizing the extra wind power consumed by coordinating the consumption quota for different types of energy sources at the system level and distributes the consumption share for every unit within each type of energy source, thereby maximizing fuel savings and economic benefits in the second step. The effectiveness of the approach is demonstrated using simulation results for an electric-heat hybrid system. Compared with two existing dispatching methods, the scheduling strategy presented in this paper could consume more extra wind power and provide higher fuel savings and economic benefits. PubDate: 2019-11-01
Abstract: Abstract Due to the uncertainty of the accuracy of wind power forecasting, wind turbines cannot be accurately equated with dispatchable units in the preparation of a day-ahead dispatching plan for power grid. A robust optimization model for the uncertainty of wind power forecasting with a given confidence level is established. Based on the forecasting value of wind power and the divergence function of forecasting error, a robust evaluation method for the availability of wind power forecasting during given load peaks is established. A simulation example is established based on a power system in Northeast China and an IEEE 39-node model. The availability estimation parameters are used to calculate the equivalent value of wind power of the conventional unit to participate in the day-ahead dispatching plan. The simulation results show that the model can effectively handle the challenge of uncertainty of wind power forecasting, and enhance the consumption of wind power for the power system. PubDate: 2019-11-01
Abstract: Abstract The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers’ benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model. PubDate: 2019-11-01
Abstract: Abstract Smart grid has integrated an increasing number of distributed energy resources to improve the efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid, e.g., households, equipped with distributed energy resources can be considered as “microgrids” that both generate and consume electricity. In this paper, we study the energy community discovery problems which identify energy communities for the microgrids to facilitate energy management, e.g., load balancing, energy sharing and trading on the grid. Specifically, we present efficient algorithms to discover such communities of microgrids considering both their geo-locations and net energy (NE) over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets. PubDate: 2019-11-01
Abstract: Abstract This paper discusses a security-constrained integrated coordination scheduling framework for an integrated electricity-natural gas system (IEGS), in which both tight interdependence between electricity and natural gas transmission networks and their distinct dynamic characteristics at different timescales are fully considered. The proposed framework includes two linear programming models. The first one focuses on hour-based steady-state coordinated economic scheduling on power outputs of electricity generators and mass flow rates of natural gas sources while considering electricity transmission N − 1 contingencies. Using the steady-state mass flow rate solutions of gas sources as the initial value, the second one studies second-based slow gas dynamics and optimizes pressures of gas sources to ensure that inlet gas pressure of gas-fired generator is within the required pressure range at any time between two consecutive steady-state scheduling. The proposed framework is validated via an IEGS consisting of an IEEE 24-bus electricity network and a 15-node 14-pipeline natural gas network coupled by gas-fired generators. Numerical results illustrate the effectiveness of the proposed framework in coordinating electricity and natural gas systems as well as achieving economic and reliable operation of IEGS. PubDate: 2019-11-01
Abstract: Abstract Due to the popularization of distributed energy resources (DERs), the aggregated prosumer effect excels a general energy storage system characteristic. Virtual energy storage system (VESS) concept is proposed hereby that mimics an actual storage unit and incorporates the same charging (consumer) and discharging (producer) modes. It is possible to provide ancillary services via VESS by exploiting the flexibility and thus much research has been proposed on the optimization of the VESS scheduling. In general, the charging and discharging efficiencies of VESS are different and there can be only one status at a time slot. To achieve the optimal schedule while considering the constraints above, binary terms should be introduced into the optimization problem which end up with a nonconvex problem. In this paper, a complimentary mathematical proof is given for the convexification of this mixed integer linear programming (MILP) problem so that the linear programming (LP) method can be applied instead if the objective function is linear. The proposed proof is validated through a case study and the simulation results show the effectiveness of the proposed method. PubDate: 2019-11-01
Abstract: Abstract The three-phase dual active bridge (3p-DAB) converter is widely considered in next-generation DC grid applications. As for traditional AC grids, the successful integration of power electronic converters in DC grids requires accurate time-domain system-level studies. As demonstrated in the existing literature, the development and efficient implementation of large-signal models of 3p-DAB converters are not trivial. In this paper, a generalized average model is developed, which enables system-level simulation of DC grids with 3p-DAB converters in electromagnetic transient type (EMT-type) programs. The proposed model is rigorously compared with alternative modeling techniques: ideal-model, switching-function and state-space averaging. It is concluded that the generalized average model provides an optimal solution when accuracy of transient response, reduction in computation time, and wideband response factors are considered. PubDate: 2019-11-01
Abstract: Abstract Increasing energy consumption has caused power systems to operate close to the limit of their capacity. The distributed power flow controller (DPFC), as a new member of distributed flexible AC transmission systems, is introduced to remove this barrier. This paper proposes an optimal DPFC configuration method to enhance system loadability considering economic performance based on mixed integer linear programming. The conflicting behavior of system loadability and DPFC investment is analyzed and optimal solutions are calculated. Thereafter, the fuzzy decision-making method is implemented for determining the most preferred solution. In the most preferred solution obtained, the investment of DPFCs is minimized to find the optimal number, locations and set points. Simulation results on the IEEE-RTS79 system demonstrate that the proposed method is effective and reasonable. PubDate: 2019-11-01
Abstract: Abstract As an emerging paradigm in distributed power systems, microgrids provide promising solutions to local renewable energy generation and load demand satisfaction. However, the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling, especially for small-scale microgrids. Instead of deploying stochastic models to cope with such challenges, this paper presents a retroactive approach to real-time energy scheduling, which is prediction-independent and computationally efficient. Extensive case studies were conducted using 3-year-long real-life system data, and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11% on average, which suggests better performance than model predictive control with the cost difference at 30% compared to the perfect dispatch. PubDate: 2019-11-01
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