Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.

Research on solar aided coal-fired power generation system and performance analysis

Integrating solar power utilization systems with coal-fired power units, the solar aided coal-fired power generation (SACPG) shows a significant prospect for the large-scale utilization of solar energy and energy saving of thermal power units. The methods and mechanism of system integration were studied. The parabolic trough solar collectors were used to collect solar energy and the integration scheme of SACPG system was determined considering the matching of working fluid flows and energy flows. The thermodynamic characteristics of solar thermal power generation and their effects on the performance of thermal power units were studied, and based on this the integration and optimization model of system structure and parameters were built up. The integration rules and coupling mecha- nism of SACPG systems were summarized in accordance with simulation results. The economic analysis of this SACPG system showed that the solar LEC of a typical SACPG system, considering CO2 avoidance, is 0.098 $/kW·h, lower than that of SEGS, 0.14 $/kW·h.

On the State-dependent Switched Energy Functions of DFIG-based Wind Power Generation Systems

This paper proposes a novel state-dependent switched energy function(SdSEF)for general nonlinear autonomous systems,and constructs an SdSEF for doubly-fed induction generator(DFIG)-based wind power generation systems(WPGSs).Different from the conventional energy function,SdSEF is a piece-wise continuous function,and it satisfies the conditions of conventional energy functions on each of its continuous segments.SdSEF is designed to bridge the gap between the well-developed energy function theory and the description of system energy of complex nonlinear systems,such as power electronics converter systems.The stability criterion of nonlinear autonomous systems is investigated with SdSEF,and mathematical proof is presented.The SdSEF of a typical DFIGbased WPGS is simulated in the whole processes of a grid fault and fault recovery.Simulation results verify the negativeness of the derivative of each continuous segment of the SdSEF.

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

Coordinated Optimization of Multi-energy Generation Systems Considering the Health Damages of Coal-fired Pollutant Dispersion

Pollutants emitted from coal-fired power plants lead to the deterioration of air quality in developing countries,and contribute to both mortality and morbidity.To improve air quality from power generation,new dispatch strategies incorporated with air pollution dispersion models should be considered.This paper takes into account the impact of meteorological variations on spatio-temporal dispersion of pollutants.Depending on the coal-fired pollutant concentration estimated by the Gaussian plume dispersion model,exposure-response functions are used to quantify the resulting health effects.Furthermore,the corresponding economic costs of health damages are incorporated to penalize the power dispatch.Considering generation costs and economic costs of health damages,this paper formulates a twostage stochastic optimization model of a multi-energy generation system including coal units,gas units,and photovoltaic stations.Finally,numerical studies based on a modified IEEE 14-node system are performed for illustration and validation.

Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Distributed generation including wind turbine(WT) and photovoltaic panel increases very fast in recent years around the world, challenging the conventional way of probabilistic load flow(PLF) calculation. Reliable and efficient PLF method is required to take this chage into account.This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation,and then develops a maximum entropy based PLF(MEPLF) calculation algorithm for power system integrated with wind power generation(WPG). Compared with traditional Gram–Charlier expansion based PLF(GC-PLF)calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions(PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve thelimitation of GC-PLF calculation method that mistakenly gains negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.

Combining solar power with coal-fired power plants,or cofiring natural gas

Coal-fired power operators continue to look for ways to increase the efficiency and extend the working lives of their plants by improving operational flexibility and reducing environmental impact.Two possible options are explored here:combining solar energy with coal-fired power generation,and cofiring natural gas in coal-fired plants.Both techniques show potential.Depending on the individual circumstances,both can increase the flexibility of a power plant whilst reducing its emissions.In some cases,plant costs could also be reduced.Clearly,any solar-based system is limited geographically to locations that receive consistently high levels of solar radiation.Similarly,although many coal-fired plants already burn limited amounts of gas alongside their coal feed,for cofiring at a significant level,a reliable,affordable supply of natural gas is needed.This is not the case everywhere.But for each technology,there are niche and mainstream locations where the criteria can be met.The need for good solar radiation means that the uptake of coal-solar hybrids will be limited.Cofiring natural gas has wider potential:currently,the largest near-term market appears to be for application to existing coal-fired plants in the USA.However,where gas is available and affordable,potential markets also exist in some other countries.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.