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While dynamic regulating effects of pumped storage units have been more and more important in power systems integrated with large-scale wind power, outputs of the conventional reversible pumped storage units still cannot be dynamically adjusted in pumping mode due to the characteristics of pump-turbine. In order to expend the regulating range of reversible pumped storage unit, the variable speed solution is adopted in this paper to realize the dynamic regulating of wind power under the pumping mode. Based on the analysis of pump turbine characteristic curves, the dynamic model and corresponding control strategy of variable speed pumped storage unit are derived. And then, the IEEE 39-bus test system is used to verify wind power regulating performance of variable speed pumped storage unit in pumping mode. The results show that variable speed pumped storage unit has broadened the regulating range of wind power by dynamically adjusting pumping power and effectively reduce the frequency deviation of power system caused by the short-time wind power fluctuation.

The output of wind power is random and uncertain. As the proportion of wind power participating in the electricity market increases, it will have a great impact on the electricity price and the profit of traditional generators. Generating the scenarios of wind power by Monte Carlo method, this paper establishes unit commitment model and economic dispatch model of electricity spot market with wind power. Based on the actual operation data of a provincial electricity market, the changes of nodal price under different wind power penetration rates and different prediction accuracy of wind power are calculated. The influencing factors of electricity price fluctuation are studied, and the impact of wind power on the profit of traditional generators is analyzed.

Clean energies such as wind energy and solar energy have increased very fast in order to meet the environmental requirements. However, due to the uncertainties of wind and solar energy, the large-scale integration of new energy presents a great challenge to the power system economic dispatch. Thus, a multi-time-scale robust economic dispatch strategy of a multi-source hybrid power system based on the variable confidence level is proposed. The deterministic constraints of each time scale are transformed into robust constraints that take the uncertainty into account. Meanwhile, the robust level whose confidence level increases with the shortening of time scale is set to improve the scheduling conservative degree step by step. The selection principle of the robust level of each time scale is also presented. The proposed approach is applied to an IEEE 9-bus system. The calculation results are compared with those from the traditional multi-time scheduling method and show the effectiveness of the paper, which can reduce the uncertainty impact of wind, solar, and load forecast, as well as achieve a great balance of security, economic, and environmental benefits.

Battery swapping is an effective way for the energy fast recharge of electric vehicles (EVs). The batteries stored in the battery swapping stations (BSSs) might be an emerging form of energy storage that can be used in the future power systems. Unlike traditional battery storage, the application of BSSs storage needs to consider the behaviour of EV users and the charging process in the BSSs. The calculation of controllable capacity (CC) plays a key role in the application of BSSs storage. This study proposes a Monte Carlo stochastic simulation method (MCSS) to estimate the CC of BSSs. The queuing problems in EV batteries swapping are simulated by the proposed MCSS method. The CC then can be obtained from the queues analysis. Simulation results demonstrate the availability of the proposed method.

With the growing maturity of wind power technologies and the decrease of wind power construction costs, wind power producers, who are always price takers, gradually become the market participants with strategic bidding capacity in electricity market. However, wind power producers should be responsible for the loss caused by bidding deviation when they participate in electricity market due to uncertainty of wind power, which can cut down their benefits. In order to study the bidding strategies of the wind power producers in day-ahead market, a wind & thermal power joint (W&T) bidding model considering penalties for wind power's bidding deviation was established based on supply function equilibrium approach, in which Monte-Carlo method and backward reduction were used to simulate future wind power. In addition, particle swarm optimization method is used to solve the model. Compared with the independent bidding, a simulation example showed that wind & thermal power joint bidding can effectively improve the revenues of wind power producers, especially increase the incomes from day-ahead market, so that the initiative of wind power producers in day-ahead market can be greatly enhanced.

Future electricity distribution grids will host a considerable share of variable renewable energy sources, such as roof photo voltaic and small wind power. The households will progressively become prosumers playing an active role on localized peer-to-peer electricity trading. This paper proposes a conceptual framework that enables households to autonomously exchange energy with neighbors based on blockchain techniques, aimed to locally balancing renewable energy production.

This paper had analyzed and modeled the wind power characteristic from the perspective of space-time series. Firstly, measured data of wind power had been analyzed for the coupled spatial-temporal correlation. Then, a spatial relation matrix, which was used to describe the location of wind farms, had been embedded into the Space-Time Auto Regressive Moving Average (STARMA) model in order to reflect the spatial-temporal correlation of multi-dimensional wind power series. Simulation results showed that this model has restored not only temporal autocorrelation, but also time shifting characteristic of spatial correlation of the original wind power series, which essentially reflected the coupled spatial-temporal characteristic of real wind power series. This model can be used to produce huge amount of simulated wind power data, which have same characteristics with real wind power data, and can provide basics for the planning and operation of wind power integration systems.

This paper discusses the implementation of a fully decentralized optimization for the multi-area stochastic dynamic economic dispatch (MASDED) of large scale power systems with wind power integrated. Firstly, the volatility of wind power is represented by sampling wind scenarios, and the consensus constraints of boundary buses’ phase angles among the forecast scenario and sampling scenarios are built. Secondly, the multi-area power system is decomposed into different areas by duplicating boarder variables. Finally, a bi-level iterative method that combines the modified analytical target cascading (ATC) method and the Benders decomposition method is applied to solve the MASDED in a fully decentralized way, and a coordination center turns out to be not necessary. The forecast scenario sub-problems and the sampling scenarios sub-problems of each area are solved iteratively, and the multipliers are updated in each area respectively. The 3-area IEEE RTS system and a 4-area 2298-bus system are tested to demonstrate the effectiveness of the proposed method.

The controller parameters of automatic generation control (AGC) system have a great impact on the dynamic performance of power system frequency control. The improper parameters of AGC controller may lead to a stability problem of grid frequency when a large load disturbance occurs. This will be even more serious in a high penetration of renewable energy power grid because of the lack of system inertia and regulation reserves. This paper presents a novel hybrid bacteria foraging optimization (BFO) and pattern search (PS) optimized fuzzy PID controller for AGC system of a two-area interconnected power system. The scaling factors of the fuzzy PID controller are optimized using the proposed hybrid BFO and PS (BFO-PS) optimization technique. The superiority of the proposed fuzzy PID controller has been shown by comparing the results with some recently published techniques for the same interconnected power system. Further, sensitivity analysis is carried out which demonstrates the ability of the proposed approach to wide changes in system parameters and operating load conditions.

The accommodation of renewable energy has become a worldwide problem restricting the development of power grid. The development of energy internet will provide a new way to solve this problem. This paper analyzed the key factors influencing the accommodation of renewable energy in our country, as well as the basic connotation of energy internet. Some accommodating technologies of renewable energy orienting to energy internet are also discussed, such as optimal dispatch, integrated energy system, market consumption and so on, to provide reference for the optimization of future power grid operation and accommodation of renewable energy.

The battery energy storage (BES) is very promising to be used for improving the performance of automatic generation control (AGC) in power system by offering fast active power compensation. However, the improper parameters of controller in AGC system may cause an unstable frequency problem. This paper presents an optimal controller design method based on bacteria foraging algorithm (BFA) for AGC system with BES. A two-area reheat thermal system is considered to be equipped with the proportional plus integral (PI) controllers. The BFA technique is employed to search for the optimum controller parameters by minimizing the integral of time multiply absolute error (ITAE) index. The performance of the proposed BFA tuning controller has been evaluated with that of the controllers tuned by particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results emphasize the performance of AGC system with BES and demonstrate the superiority of the proposed BFA tuning controller compared to the optimized controller based on PSO and GA.

Generation and Transmission equipment maintenance scheduling (GTEMS) involves many aspects in power systems operation. Unreasonable arrangements will bring a lot of adverse effects. This paper studies the preventive maintenance. Through the regular inspection (preventive maintenance), the equipment's life span can be extended. This paper develops an optimal model of multi-objective optimization model for generation and transmission equipment maintenance scheduling considering load transfer in case of heavy load or equipment outage. Four objectives are considered for optimal maintenance scheduling. The solution method is a relatively dominant objective of immune genetic algorithm. The proposed multi-objective optimization GTEMS model and approach are tested on IEEE 30 bus system. The test results show the effectiveness of the paper.