Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.

Operation Strategy Analysis and Configuration Optimization of Solar CCHP System

This paper proposed a new type of combined cooling heating and power(CCHP)system,including the parabolic trough solar thermal(PTST)power generation and gas turbine power generation.The thermal energy storage subsystem in the PTST unit provides both thermal energy and electrical energy.Based on the life cycle method,the configuration optimization under eight operation strategies is studied with the economy,energy,and environment indicators.The eight operation strategies include FEL,FEL-EC,FEL-TES,FEL-TES&EC,FTL,FTL-EC,FTL-TES,and FTL-TES&EC.The feasibility of each strategy is verified by taking a residential building cluster as an example.The indicators under the optimal configuration of each strategy are compared with that of the separate production(SP)system.The results showed that the PTST-CCHP system improves the environment and energy performance by changing the ratio of thermal energy and electric energy.The environment and energy indicators of FEL-TES&EC are superior to those of FTL-TES&EC in summer,and the results are just the opposite in winter.The initial annual investment of the PTST-CCHP system is higher than that of the SP system,but its economic performance is better than that of the SP system with the increase of life-cycle.

Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

On the Short-Term Optimisation of a Hydro Basin with Social Constraints

In this paper, an optimisation problem for calculating the best energy bids of a set of hydro power plants in a basin is proposed. The model is applied to a real Spanish basin for the short-term (24-hour) planning of the operation. The algorithm considers the ecological flows and social consumptions required for the actual operation. One of the hydro plants is fluent, without direct-control abilities. The results show that the fluent plant can be adequately controlled by using the storage capacities of the other plants. In the simulations, the costs related to the social consumptions are more significant than those due to the ecological requirements. An estimate of the cost of providing water for social uses is performed in the study.

Power System Flow Adjustment and Sample Generation Based on Deep Reinforcement Learning

With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.

Control of hybrid AC/DC microgrid under islanding operational conditions

This paper presents control methods for hybrid AC/DC microgrid under islanding operation condition.The control schemes for AC sub-microgrid and DC sub-microgrid are investigated according to the power sharing requirement and operational reliability.In addition,the key control schemes of interlinking converter with DC-link capacitor or energy storage,which will devote to the proper power sharing between AC and DC sub-microgrids to maintain AC and DC side voltage stable,is reviewed.Combining the specific control methods developed for AC and DC sub-microgrids with interlinking converter,the whole hybrid AC/DC microgrid can manage the power flow transferred between sub-microgrids for improving on the operational quality and efficiency.

浅谈1500kW风电机组运行规程的编制

针对不同厂家设备的技术要求差异,对1500kW风电机组运行规程进行规范化编制,达到提高安全生产监控能力和机组运行可靠性的目的。

Cascade utilization of full spectrum solar energy for achieving simultaneous hydrogen production and all-day thermoelectric conversion

Solar-driven photocatalytic water/seawater splitting holds great potential for green hydrogen production.However,the practical application is hindered by the relatively low conversion efficiency resulting from the inadequate utilization of solar spectrum with significant waste in the form of heat.Moreover,current equipment struggles to maintain all-day operation subjected to the lack of light during nighttime.Herein,a novel hybrid system integrating photothermal catalytic(PTC)reactor,thermoelectric generator(TEG),and phase change materials(PCM)was proposed and designed(named as PTC-TEG-PCM)to address these challenges and enable simultaneous overall seawater splitting and 24-hour power generation.The PTC system effectively maintains in an optimal temperature range to maximize photothermal-assisted photocatalytic hydrogen production.The TEG component recycles the low-grade waste heat for power generation,complementing the shortcoming of photocatalytic conversion and achieving cascade utilization of full-spectrum solar energy.Furthermore,exceptional thermal storage capability of PCM allow for the conversion of released heat into electricity during nighttime,contributing significantly to the overall power output and enabling PTC-TEG-PCM to operate for more than 12 h under the actual condition.Compared to traditional PTC system,the overall energy conversion efficiency of the PTC-TEG-PCM system can be increased by∼500%,while maintaining the solar-to-hydrogen efficiency.The advancement of this novel system demonstrated that recycling waste heat from the PTC system and utilizing heat absorption/release capability of PCM for thermoelectric application are effective strategies to improve solar energy conversion.With flexible parameter designing,PTC-TEG-PCM can be applied in various scenarios,offering high efficiency,stability,and sustainability.

Distribution Control Centers in the US and Europe:Commonalities,Differences,and Lessons

The distribution control center(DCC)has evolved from a sideshow in the traditional distribution service center to a major centerpiece of the utility moving into the decentralized world.Mostly,this is the place where much of the action is happening due to new forms of energy that are coining into the distribution system.This creates the flexibility of operation and in-creased complexity due to the need for increased coordination between the transmission control center and DCC.However,the US and European utilities have adapted to this change in very different ways.Firstly,we describe the research works done in a DCC and their evolutions from the perspectives of major US utilities,and those enhanced by the European perspective focusing on the coordination of distribution system operator and transmission system operator(DSO-TSO).We pres-ent the insights into the systems used in these control centers and the role of vendors in their evolution.Throughout this paper,we present the perspectives of challenges,operational capabilities,and the involvement of various parties who will be re-sponsible to make the transition successful.Key differences are pointed out on how distribution operations are conducted between the US and Europe.