Scenario-based Unit Commitment Model Considering Stochastic Wind Power

Based on the study of uncertainty and fluctuation of wind power, a novel algorithm including bad operation scenarios set and robust optimization method was proposed in the paper trying to solve probabilistic unit commitment problem of power system which has wind power generation. According to the power system probabilistic dispatch theory, the conservative indicator to evaluate the value of deterioration for bad scenarios set is established, and the variance is added in the optimization target to suppress some individual bad scenarios which may lead to deterioration of the overall optimization results. Furthermore, energy storage system, which has an inherent ability to mitigate active power fluctuation, was taken into account to contribute to the presented unit commitment models. Finally, the numerical example which includes cost, storage capacity, robustness metric and conservatism degree analysis verifies the effectiveness and practicality of the proposed methods.

Assuming that all electric vehicles (EVs) integrated to the grid are managed by aggregators, it is possible to make full use of the controllability of the charging EVs. Based on this, the charging load of each EV aggregator is defined as one of the optimal scheduling variables in the unit commitment (UC) model. This can be accomplished by assessing the upper and lower limit of charging load of each EV aggregator in different scenarios, which is simulated by Monte Carlo method. According to this, a UC model is proposed for optimal coordination of EV aggregators and wind generation, considering the EV owner's requirement, the range of charging load and the unit constraints. Using the10-units system as the test system, the results of UC models with different EVs charging strategies and the wind power integration indicators such as utilization rate are investigated. Simulation results show that both the abandoned wind power and the unit operating costs can be decreased more effectively in the proposed UC model.

Difference between grid connections of large-scale wind power and conventional synchronous generation

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

Low-carbon electric power systems are often characterized by high shares of renewables, such as wind power. The variable nature and limited predictability of some renewables will require novel system operation methods to properly size and cost-efficiently allocate the required reserves. The current state-of-the-art stochastic unit commitment models internalize this sizing and allocation process by considering a set of scenarios representing the stochastic input during the unit commitment optimization. This results in a cost-efficient scheduling of reserves, while maintaining the reliability of the system. However, calculation times are typically high. Therefore, in this paper, we merge a state-of-the-art probabilistic reserve sizing technique and stochastic unit commitment model with a limited number of scenarios in order to reduce the computational cost. Results obtained for a power system with a 30% wind energy penetration show that this hybrid approach allows to approximate the expected operational costs and reliability of the resulting unit commitment of the stochastic model at roughly one thirtieth of the computational cost. The presented hybrid unit commitment model can be used by researchers to assess the impact of uncertainty on power systems or by independent system operators to optimize their unit commitment decisions taking into account the uncertainty in their system.

Wind power is also known as junk. This is because wind power fluctuations affect the security and stability operation. Wind power wind turbines created is mainly concerned with the speed of wind. Because of the wind direction uncertain, intermittent, and wake effects between each unit wind farm, wind turbines cannot make that kind of power according to the demand for energy as conventional generators. Due to the lack of experimental data, assess the volatility of wind power is still a lack of effective methods. This article studies the sample in a northeast wind farm power, and based on a sliding differential algorithm, distribution fitting and quantitative calculations describe the characteristics of wind power fluctuations. This article studies the sample in a northeast wind farm power, and based on a sliding difference algorithm, through the analysis showed that wind power fluctuations obey t location scale distribution. And it is affected by factors such as spatial and temporal distribution, there is a big difference between the output power fluctuation characteristics of wind farm output power and single wind turbine. This is due to the wind turbine suffered varying differences, and wake effects between field units, making the distribution of frequent power fluctuations; relative to a single unit, the fluctuation of the whole wind farm is more gentle, that is to say with the spatial distribution increased scale, wind power fluctuations presents certain "gentle effect."

Wind power is a kind of clean and renewable energy, and it is of great benefit to utilize it in an industrial micro-grid. This paper establishes an economic dispatch model of industrial micro-grid with wind power that aims at minimizing energy costs based on scenario tree. The uncertainty of wind power and load are managed by adjusting the production schedule and unit generation schedule properly. A numerical test is taken with data of an industrial micro-grid in the paper, and the result indicates that the fluctuation of wind power and load can be mitigated by production and power generation schedule effectively and utilizing wind power can help decrease the energy costs largely. The model is also compared with the deterministic model, and some conclusions are obtained.

The anti-peak shaving characteristics of wind power is an important factor that limits the consumption of wind power. The use of the space-time translation capability of a battery energy storage system is one of the effective means for promoting wind power consumption. Thus, this study proposes an energy storage system smoothing wind power fluctuation control strategy considering wind power consumption to improve the utilization level and economy of an energy storage system. First, the statistical characteristics of wind power are analyzed, and the scene division principle of a multiscenario regulation of an energy storage system is formulated using a correlation coefficient. On this basis, the charging and discharging intervals of the energy storage system in each scenario are determined by comprehensively considering the wind power and the time domain distribution law of load. Then, the control strategy of energy storage system based on inter-zone control is proposed based on the division of charging and discharging intervals. During the charging interval, smoothing the upward fluctuation of wind power or reducing the amount of abandoned wind power is aimed at storing electricity. During the discharge interval, electricity is released to smooth the downward fluctuation of wind power. Finally, considering the real-time state of charge and wind power consumption of the energy storage system, a method of charging and discharging power correction based on fuzzy control is proposed. Taking the actual data of a wind farm as an example, a test is performed on a simulation platform of a combined wind storage power generation system to verify the feasibility and superiority of the proposed control strategy.

As wind power has characteristics of volatility, uncertainty, and poor predictability, if the penetration rate of wind power is to be improved, sufficient flexible and adjustable power supply is required to absorb large-scale wind power. Therefore, the reasonable proportion of wind power, flexible and adjustable power supply and common coal-fired power supply shall be considered in the generation and transmission coordinated planning. In this paper, the K-means clustering method is used to sample the load and wind speed values at different times of typical days from the actual data. Based on the peak and valley of net load on several typical days in each year during the planning period, a dynamic generation and transmission coordinated planning mathematical model that can simultaneously consider wind power, gas-fired power generation and coal-fired power generation is established. The objective of this model is to make the optimal investment for the candidate units and lines under the premise of satisfying the constraints of safe operation of the power grid, to ensure that the total present value of the investment cost and the operation cost in the planning period is minimum. At the same time, the simplified unit commitment model is used to simulate the dispatching operation of various types of units in the power system in typical days, to explain the flexible regulation function of gas-fired generating units on wind power and load fluctuation.

Due to the intermittency and fluctuation of wind power(WP) and photovoltaic(PV), it is difficult to estimate their reasonable values accurately when calculating the power balance. Hence, an equivalent reliable capacity calculation method of wind and photovoltaic power considering reserve is proposed in this paper, which takes the minimal load shedding as the objective function and constraints the model in power balance, output of existing generators and additional reserve operation. System reserve capacity is added based on the certain gradient and Monte Carlo Sampling(MCS) is adopted to determine the upper bound of the existing units. Finally, the equivalent reliable capacity of WP and PV in east China Power Grid is calculated by empirical analysis. Moreover, the impact of energy storage(ES) on WP and PV credible capacity is analyzed.

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

The random fluctuation and anti-peaking characteristics of wind power has brought new problems for the power system optimal dispatch. Based on the interaction characteristic of the load, this paper played the utility of interactive load which can help system consumers the positive and negative fluctuations of wind power, and considered interactive load as a scheduling resource into the traditional day-ahead scheduling model. Taking into account the effects of interactive load on system operating costs and power flow distribution, this paper established a generation scheduling model which the aim are both the system operating costs and network loss minimization in large-capacity wind power integrated system, and reformulated the multi-objective optimization problem into a single objective nonlinear programming problem by means of the fuzzy theory, and made the generation side and the demand side of the power grid can participate optimal allocation of resources, and provided a new ideas and methods for achieving source and load interactive in smart grid environment. The simulation on IEEE 30-bus system indicated this method can reduce system operating costs and network losses effectively, and improve wind power consumptive level as well as too.

With the large-scale wind power integration, the uncertainty of wind power poses a great threat to the safe and stable operation of the system. This paper proposes dynamic economic dispatch problem formulation in thermal power system incorporating stochastic wind and small-hydro (run-in-river) power, called thermal-wind-small hydropower system (TWSHS). Weibull and Gumbel probability density functions are used to calculate available wind and small-hydro power respectively. An improved differential evolution algorithm based on gradient descent information (DE-GD) is proposed to solve the dynamic economic dispatch (DED) problem considering uncertainty of wind power and small-hydro power, as well as complicated constraints in TWSHS. Based on the traditional differential evolution algorithm, the gradient information of the objective function is introduced after the mutation process to enrich the diversity of the population, thus increasing the possibility of convergence to the global optimization. Generation scheduling is simulated on a TWSHS with the proposed approach. Simulation results verify feasibility and effectiveness of the proposed method while considering various complex constraints in the thermal-windsmall hydropower system.

Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles

Owing to the stochastic and intermittent of output power of wind farms, the large-scale grid-connected wind power could have a remarkable impact on the power system operation. In this study, wind power fluctuation (WPF) mitigation is achieved from the perspective of power system low-frequency oscillation (LFO). Firstly, in order to meet fluctuation mitigation requirement, model predictive control algorithm is applied to smooth WPF. Secondly, the Hilbert–Huang transform method is used to obtain the influences of wind power before and after smoothing on system LFO. In addition, wind power is decomposed into high, medium and low bands by empirical mode decomposition, and then the impacts of different bands wind power on system LFO are studied. Finally, the influences of wind power with different fluctuation rates on system LFO are analysed based on the WSCC 3-machine 9-bus power system, which provides a theoretical reference for smoothing WPF.