The integration of large-scale distributed generation requires dynamic economic dispatch to reduce the total operational cost of active distribution networks. With the wide penetration of renewable energy in distribution network, great challenges have been addressed on the accuracy of economic dispatch with the concern of uncertainty. This paper proposes a stochastic dynamic economic dispatch model of active distribution network, in which the uncertainties of distributed generation output and load forecasting are incorporated. This model becomes a chance-constrained optimization problem and cannot be directly solved. In this paper, authors describe a relaxation algorithm to relax it as a deterministic mixed second order cone programming model. Numerical tests on IEEE 33-node distribution network shows that the model can be effectively solved with predefined constraint-violating-rate, and this method outperforms the traditional robust optimization model.
With the increasing scale of power network, many steady-state power quality (PQ) problems are caused by multiple loads connected to the distribution network. The prediction of voltage quality according to the monitoring data in distribution stations is helpful to grasp the change trend of power quality level, which is of great significance for early warning and governance of power quality. In order to effectively analyze the change law of steady-state data and improve the level of power quality, a new method for voltage quality steady-state indices prediction is proposed. The method is based on long short-term memory (LSTM) network. It mines and uses the correlation of different time series data and optimizes the prediction effect of steady-state indices. Firstly, the correlation between active power and voltage quality steady-state indices is analyzed, and then their time series characteristics are matched by temporal correlation. Secondly, LSTM network is used to model correlation relationship between selected active power and actual steady-state monitoring data for voltage quality. Finally, the forecasting model is used to predict the voltage quality steady-state data of an area of Fujian power grid. According to experimental results, the DTW-LSTM model constructed by combining with the specific user's electricity consumption behavior can improve the short-term prediction accuracy of voltage quality steady-state indices under both relatively constant and changing daily electricity consumption behavior of users. Moreover, the long-term predictions are more effective in the second case.
Applying shunt active power filter (SAPF) and dynamic voltage restorer (DVR), a hybrid power system including photovoltaic and wind turbine is built in this paper to observe power quality (PQ) disturbances caused by the distributed generation connecting to the distribution network. In addition, control algorithms such as fuzzy logic, neural network and adaptive neural fuzzy inference system are employed to optimize the dynamic performance of SAPF, to control power quality disturbances, to achieve maximum power point tracking (MPPT) in both photovoltaic and wind energy systems by adopting artificial intelligence technology. Finally, the harmonic distortion rates of the linear and nonlinear loads on the output side are reduced to 0.20% and 2.05% in the simulation system, respectively, which meet the power quality requirements in power distribution system.
Large-scale electric vehicles (EV) accessing to the system brings opportunities and challenges to the scheduling of integrated energy system. With the power-heat-gas multi-energy flow and the scheduling flexibility of electric vehicles taken into consideration, this paper establishes a two-level scheduling model for integrated energy system with electric vehicles, performing grouped and hierarchical scheduling to make a rational charge-discharge strategy for each EV. Minimum scheduling plan cost, the smallest energy fluctuation and the best environmental protection are taken as objective functions in the scheduling plan level. Improved multi-objective particle swarm optimization (MOPSO) algorithm is adopted to solve the day-ahead scheduling plan. The EV scheduling level uses particle swarm optimization (PSO) algorithm to make the charge-discharge plan of each EV cluster with the goal of user satisfaction. According to the dynamic priority, the charge-discharge strategy of each EV in the cluster is solved. The analysis of case shows that the established scheduling model can effectively solve the scheduling problem of integrated energy system with electric vehicles, which has less calculation dimension, shorter simulation time and better practicality.
The integrated energy system (IES) has the characteristics of flexible scheduling and mutual aiding among multiple energies. Aiming at the current insufficient research on the reliability of IES energy supply, an IES reliability evaluation method considering optimal load reduction and thermal load inertia is proposed. The expected index of the total loss of the IES from power outage is established, which fully reflects the economic loss caused by the power outage of the IES. Considering energy grade, load importance, and thermal load inertia, an optimal load reduction model for IES is established, which takes into account the minimum operating cost and the minimum weighted outage load under failure scenarios. The CPLEX software is applied to solve the proposed problem in order to obtain the electric and thermal load reduction power and component equipment output. According to the principle of sequential Monte Carlo simulation, an IES reliability assessment method and its process are designed. The calculation example analysis shows that the reliability level of IES can be improved effectively by using optimal load reduction strategy and considering thermal load inertia.
Because of the difficulties of biomass fuel collection and unstable supply in the actual operation of the integrated energy system (IES) centered on biomass cogeneration units, this paper proposes an IES planning method that considers the price elasticity of biomass supply. Firstly, according to the biomass purchase price elasticity theory, a straw collection cost model is established and introduced into the two-level planning of the industrial park IES. Secondly, the upper-level model uses equipment planning capacity and straw purchase price as optimization variables to pursue the minimum annualized comprehensive cost; the lower-level model uses equipment hourly output and straw consumption as optimization variables to pursue the minimum annual operating cost that takes into account the price flexibility of straw collection costs, and the particle swarm optimization and the CPLEX toolbox are used to solve the problem. Finally, a typical industrial park in Jilin Province is taken as an example to verify that the proposed planning method is more suitable for the actual situation of the project and has a more superior economic efficiency.
With the rapid development of microgrid systems with multiple intelligent buildings integrated, thermostatically controlled loads (TCLs) have gradually become an important demand-response resource. However, its distribution dispersion and response uncertainty also put forward higher requirements for the regulation. To address the distribution dispersion of TCLs, this paper puts forward a TCLs aggregation scheme based on the building's resistance-capacitance (RC) model. Specifically, the RC heat transfer model is linearized at first, and then the TCLs are aggregated according to the law of large numbers, thence the building's physical structural parameters can be taken into account. This TCLs aggregation scheme not only adapts to the differentiated modeling requirements of buildings but also has preferable calculation efficiency. On the basis of the proposed TCLs aggregation scheme, a two-stage robust optimization model applicable to the integrated building microgrid system is established, which considers the uncertainties of aggregated TCLs and distributed renewable generators, and is solved by the column and constraint generation algorithm. Results show that the proposed TCLs aggregation scheme can reach the accuracy requirements of microgrid scheduling operation under diverse outdoor temperatures and solar radiation. Meanwhile, the established two-stage robust optimization model can effectively reduce the power regulation of tie lines under real circumstances, thence enhancing the stability and robustness of the microgrid system.
How to guide the massive distributed load-side resources to participate in the regulation and operation of the power grid has become a new challenge for the power system. However, there is still a lack of in-depth research on collaborative control methods for massive heterogeneous loads. For this reason, a control method of heterogeneous load cluster considering controllable margin of flexible load is proposed. Firstly, a generalized energy storage aggregation model through air conditioning load, electric vehicle charging load, and distributed energy storage is established. Secondly, at the aggregator-load equipment level, aiming at the heterogeneous characteristics of multiple types of loads, a general state-serialization control strategy based on controllable margin indicators is proposed. At the dispatch center-aggregator level, the optimization models of day-ahead and intra-day peak-regulation are established. In the day-ahead stage, considering the start and stop plan of conventional units, electric vehicle charging plan and air-conditioning interruption plan, a scheduling model with the goal of optimal day-ahead system operation economy is established. In the intra-day stage, distributed energy storage of aggregators further copes with the uncertainty of wind power and load forecasting. Finally, a case study is used to realize the peak shaving and valley filling of the system while reducing the operating cost, which verifies the feasibility and effectiveness of the control strategy and peak shaving optimization method.
This paper presented a bottom-up self-organizing aggregation operation scheduling method for virtual power plants, aiming at reducing the amount of regulation in the process of scheduling by dynamic self-organization aggregation among distributed energy resources (DER). Firstly, according to the output characteristics of DER, the consistency between which and the load is evaluated with the tracking coefficient, and the relevant optimal control model is given. As the basis of self-organizing aggregation, the basic intelligence of DER is considered. Secondly, the framework of coalition formation games is introduced, and the aggregating utility function and its corresponding benefit sharing mechanism are proposed, on the basis of which the self-organizing aggregation conditions of DER are established. Finally, according to Pareto rules, the "merge-split" mechanism is proposed, which can promote the orderly evolution of virtual power plants and form a self-organizing aggregation operation scheduling(SAOS) for virtual power plants. The results show that SAOS can be constituted dynamically according to the output characteristics of DER. Its overall scheduling quantity is equivalent to that of centralized optimization, and is significantly reduced compared with that of independent optimization. The calculation amount and calculation time of SAOS are equivalent to those of independent optimization, and significantly lower than centralized optimization.
A coordinated control for islanded DC microgrid considering power sharing of multiple energy storages is proposed to realize the balance control of the energy utilization maximization and the reasonable power sharing among multiple energy storages. The operation of the DC Microgrid is divided into five working modes according to the DC bus voltage. During mode transition, control structure of each unit keeps unchanged, and its droop curve can be adjusted adaptively according to current operating state. The coordinated operation of source and storage maintains the system power balance and bus voltage stability, which maximize the utilization rate of photovoltaic energy and ensures sufficient output of energy storage. Firstly, an adaptive power control strategy is applied to PV converters, which is based on automatic regulate the output power of PV tracking or off the maximum power point according to the DC bus voltage and realizes the smooth switching between maximum power point tracking (MPPT) mode and off-MPPT mode. Secondly, an adaptive droop control strategy based on the state of charge is designed for energy storages, which realizes the reasonable distribution of system power by multiple energy storage units according to their SOC and avoids excessive discharge or depth charge. Finally, the control strategy is verified by simulation with Matlab/Simulink.
DC transmission line breakage fault is one of the common types of DC faults, so research on the transient response characteristics of offshore wind power sending systems via flexible high voltage direct current (HVDC) transmission system in this scenario has important value for the protection scheme design. At first, the mechanism of short-circuit current under line breakage fault at the positive pole of the DC bus is analyzed in this paper. Furthermore, the expression of short-circuit current at the non-fault pole is derived, and the impact of ground-impedance on the characteristics of short-circuit current is considered. Secondly, the transient voltage evolution characteristics during the fault on the AC and DC sides of the converter station on the wind farm side and the grid side are studied. Results show that the DC voltage on the wind farm side is positively correlated with its active power output during the fault, and the AC-side voltage of the converter stations produce a pair of DC bias components of equal magnitude and opposite directions. In addition, it can effectively reduce the impact of short-circuit current on system operation performance if a larger ground-impedance is adopted. Finally, the electromagnetic transient model of the whole system is built on the PSCAD/EMTDC simulation platform, and the correctness of the analysis on transient characteristics and the rationality of parameter selection are verified by simulations. The work in this paper has certain guiding significance for the planning and design of large-scale offshore wind power integration with flexible HVDC transmission system.
Many studies about multi-terminal VSC systems are concentrated on calculating droop control coefficients. However, few methods have been used to obtain the coefficients applying the virtual resistance. According to the I-V droop control, a new P-V droop control method based on virtual resistance is proposed. Firstly, the relationship between the voltage on the DC side of VSC stations and the voltage of the common bus is analyzed in a radial DC system. Then, the relationship between VSC arm voltage and the DC voltage is obtained by imposing a virtual resistance. Considering the P-V droop equation, the method for calculating coefficients is given. Thereafter, the calculation limitations of the proposed method in extreme operating conditions are analyzed, and the resolvent is proposed which improves the control strategy. Finally, three types of control strategies, i.e., the ratio droop control, the adaptive droop control and the droop control based on virtual resistance, are simulated on a six-terminal radial VSC system. Simulation results show that, the proposed method has the advantages of smaller deviation of dc-side voltage and reasonable power distribution.
Establishment of electricity market separates the links among generation, transmission, distribution and sales. The grid planning will face many uncertain market factors. The transmission grid planning in the electricity market faces more difficulties and challenges than the traditional transmission grid planning. This paper proposes a two-layer optimal configuration model of a transmission system that considers electricity trading signals. The upper model mainly considers the investment cost of investors, while the lower model considers the mathematical model of the node's marginal electricity price under line congestion, uses the transmission congestion surplus as the objective function, and uses the locational marginal electricity price to guide the transmission system expansion planning. Aiming at the shortcoming of the slow convergence speed of the dynamic differential evolution algorithm, this paper combines the advantages of different updating strategies and proposes an improved dynamic differential evolution algorithm to solve the transmission network planning model. Finally, taking a 500/220 kV transmission network in western China as an example, simulation results verify the effectiveness and feasibility of the model and algorithm.
Wind power has low-carbon characteristics and can promote emission reduction in the energy industry. However, the problem of wind curtailment in China is still very serious. In order to promote the accommodation of wind power and obtain profit as high as possible for wind power producers, a two-level planning model for wind power declaration and market clearing in the day-ahead market of the integrated energy system has been established. Wind power declaration takes the maximum expected profit as the objective function, and the clearance model takes the maximum social welfare as the objective function. The model optimizes and improves the market mechanism and introduces carbon trading to promote emission reduction. In view of the uncertainty of wind power generation, the scenario method is proposed to make the calculation faster. The proposed model was simulated, and the influence of market mechanism and reporting strategy on the profit of wind power companies and wind power accommodation is explored.
With the proposal of achieving carbon neutral in 2060, the installed capacity and proportion of renewable energy power units in China is expecting to grow rapidly. However, due to the randomness and reverse peak-shaving characteristic of renewable energy, the reliability of power system operation faces great challenge. As a vital flexible adjusting resource, the development of demand response is conducive to improve the safety and flexibility of power system operation. Currently, many provinces have carried out various distinctive demand-response pilot projects. With the rapid development of China's power market, it is extremely urgent to realize the connection between demand response and China's power market and to establish a market-based mechanism for demand response. According to the worldwide development experience of demand response, this paper deeply analyzes the development status and challenges of demand response in China, and designs a market mechanism of demand response in combination with the construction path of China power market, which provides feasible ideas for the future development of demand response in China.
