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Due to lack of data or the complex statistical characteristics, it is difficult to ascertain the probability distribution of new energy and various loads when making dispatch decisions. A day-ahead optimal dispatch method of power system considering source and load interval uncertainties is proposed. Firstly, the interval number models of wind power, photovoltaic and various loads are established based on the prediction error. Secondly, taking the daily comprehensive operating cost as the objective function, and considering the constraints of generators, energy storage, and positive and negative backup, a mathematical model of interval optimal dispatch is constructed. Then, by applying interval optimization theory, the interval optimization problem is transformed into a deterministic problem under a certain interval possibility to solve, and the interval optimization dispatch scheme is obtained. Finally, the proposed method is applied in the modified IEEE 10-generator and 39-bus system to validate its effectiveness. The results show that the proposed method is flexible in decision-making. By adjusting the interval possibility degree, the economy and security of the dispatch scheme can be well balanced, and the amount of calculation is small. It is especially suitable for solving the optimal dispatch problem that only grasps the variation range of variables.

In northern China, thermal power units (TPUs) are important in improving the penetration level of renewable energy. In such areas, the potentials of coordinated dispatch of renewable energy sources (RESs) and TPUs can be better realized, if RESs and TPUs connected to the power grid at the same point of common coupling (PCC) are dispatched as a coupled system. Firstly, the definition of the coupled system is introduced, followed by an analysis on its characteristics. Secondly, based on the operation characteristics of deep peak regulation (DPR) of TPUs in the coupled system, the constraint of the ladder-type ramping rate applicable for day-ahead dispatch is proposed, and the corresponding flexible spinning reserve constraint is further established. Then, considering these constraints and peak regulation ancillary services, a day-ahead optimal dispatch model of the coupled system is established. Finally, the operational characteristics and advantages of the coupled system are analyzed in several case studies based on a real-world power grid in Liaoning province, China. The numerical results show that the coupled system can further improve the economic benefits of RESs and TPUs under the existing policies.

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In this paper, the probabilistic model of the controllable distributed generation in active distribution network is developed and applied to the daily stochastic optimal dispatch. The probabilistic characteristics of photovoltaic power generation system with active control capability are explored, and the relationship between the reference value of active power and its cumulative distribution function and mean value is obtained. The active power probability model of wind power generation system is improved according to the actual wind speed power curve. By fully utilizing the inverter capacity and coordinating active power, the reactive power of distributed generation is actively controlled under the constraint of power factor. Then considering the chance constraints, a daily optimal scheduling model for active distribution network with the goal of minimizing the operating cost of distribution network is developed, and the constraints that can calculate the charge and discharge times of the energy storage system are designed. The chance constrained programming is solved by the heuristic method, and the deterministic optimization steps are solved by the second-order cone programming method, respectively. The probabilistic power flow method based on stochastic response surface method is utilized to test chance constraints. Finally, the modified IEEE33 node distribution system example shows that the obtained models and algorithms are correct and can meet the requirements of safe and economic operation.

Demand-side management (DSM) can transfer the dynamic fluctuations of load to match the renewable energy sources and realize the interaction of source-load. In most existing combined cooling heating and power (CCHP) system scheduling, user-side and system-side resources are independently optimized to achieve optimum economic performances without efficiently combining resources. In this paper, the devices with adjusted loads on the system-side and the user-side are integrated to construct generalized energy storage (GES) model, including traditional flexible resources, batteries, electric vehicles, cooling and heating load response, and electro-to-thermal conversion devices. Then, the GES model is integrated into the system for optimization including economy, load fluctuation, energy efficiency, and exergy efficiency. A case study demonstrates the application of the proposed method. The scheduling results of four DSM cases under different objectives are comprehensively obtained and compared. The optimized schemes are assessed in multi-dimension decision-making method and the effects of objective weights on decision results are analyzed. The simulation results illustrate that the load variation is decreased by 54% to obtain a smooth electrical load, resulting in the stable operation of equipment and improving the security and stability of the CCHP system. The exergy efficiency, energy efficiency, and economy performances through the DSM optimization are improved by up to 8.7%, 2.5%, and 2.4%, respectively. The system optimized with the objective of load fluctuation achieves the highest score in the TOPSIS decision-making scenarios and has the best comprehensive performance. [Display omitted] • A multi-dimension day-ahead scheduling optimization model is proposed. • The transformation model between rigid and flexible electric load is built. • The multi-dimensional performance indicators are applied to demand response. • The load variance index has the most prominent performance improvement, 54%. • The exergy efficiency, energy efficiency and cost boosted by 8.7%, 2.5% and 2.4%, respectively. [ABSTRACT FROM AUTHOR]

Large-scale integration of renewable power presents a great challenge for day-ahead dispatch to manage renewable resources while provide available reserve for system security. Considering zonal reserve is an effective way to ensure reserve deliverability when network congested, a random day-ahead dispatch optimization of wind integrated power system for a least operational cost is modeled including zonal reserve requirements and N − 1 security constraints. The random model is transformed into a deterministic one based on the theory of chance constrained programming and a determination method of optimal zonal reserve demand is proposed using the minimum confidence interval. After solving the deterministic model, the stochastic simulation is conducted to verify the validity of solution. Numerical tests and results on the IEEE 39 bus system and a large-scale real-life power system demonstrate the optimal day-ahead dispatch scheme is available and the proposed method is effective for improving reserve deliverability and reducing load shedding after large-capacity power outage. [ABSTRACT FROM AUTHOR]

The intermittency and uncertainty output of renewable energy source (RES) poses a challenge for the optimal dispatch of an integrated energy system (IES). Many current studies focus on the time-domain characteristics of the RES prediction, while ignoring the time-frequency characteristics of the prediction, thus the operation is usually scheduled on a uniform time-step. Based on this context, an adaptive hybrid dispatch time-step (AHDT) determination method based on the time-frequency characteristics of the prediction is proposed. Different from the traditional time-step, AHDT is non-uniform. Firstly, the Hilbert-Huang transform is used to convert the predicted RES output data to the time-frequency. Next, a time-step function which reflects the mapping of time-step and instantaneous frequency is constructed to suggest an adaptive time-step in each dispatch segment. Then, an optimal day-ahead dispatch model of a typical IES with AHDT, which is essentially a mixed integer quadratic programming (MIQP) problem, is established. Finally, the day-ahead schedules of each unit are obtained by solving the MIQP problem. The simulations are conducted in a total of twenty randomly selected days. The results show that the AHDT method is superior to the uniform time-step method for its effect in reducing the operating cost and the optimization-calculating time cost. • An adaptive hybrid dispatch time-step (AHDT) determination method is proposed. • AHDT based on time-frequency features of predicted data is non-uniform. • Time-frequency features of predicted renewable source output is extracted by HHT. • The method is proposed for day-ahead dispatch of integrated energy systems. • The simulations conducted in 20 random days show the performance in cost-saving. [ABSTRACT FROM AUTHOR]