Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Analyses of Current Electricity Price and Its Changing Trend Forecast in the Coming Five Years

This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period should satisfy the requirements of power industry restructuring.Therefore, it is necessary to set up an appropriate pricing mechanism and system including thelinks of sales price to network, transmission and distribution price (T&D price) and sales price.In the light of various factors influencing increase and decrease in price, a forecast of electricitytariff is given in the five years to come.[

Environmental Water Price Based on Water Environment Capacity and Ecological Water Consumption: A Case Study of Hefei

With the development and reform of water price policy, the most common view is that the total cost of water price can reflect the tradable traits of water resources, which can enhance the water use efficiency and achieve water resources sustainable utilization. Besides in actual pricing operation, from the angle of protection cost, water price characterizes the cost of controlling pollution and protecting water environment. Based on the two factors of water environment capacity and ecological water consumption embodied by the environment water price, this paper puts forward an effective environmental water price calculation model based on three elements of sewage treatment fee, ecological water price and water quality maintenance fee. And this paper takes the data of Hefei as an example, calculates reasonable environmental price, comparing which with the existing price, and finds the main existing problem which needs to be solved urgently in management.

A Short-Term Electricity Price Forecasting Scheme for Power Market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.

Qingdao City Resources Water Price Estimate

The water resource price is currently one of the difficult problems of water price research.After references collection,investigation and analysis,the water resource price of Qingdao City was calculated by applying fuzzy mathematics.According to the present situation and region characteristic of Qingdao,water quality,per capita water supply,economical level,population density and alternative water resources were selected as water resource influence factors and weigh coefficients of all these factors were ascertained.The results show that the water resource price of Qingdao is 3.05yuam/m3 and total water price is 4.65Yuan/m3.

An electricity price model with consideration to load and gas price effects

Some characteristics of the electricity load and prices are studied, and the relationship between electricity prices and gas (fuel) prices is analyzed in this paper. Because electricity prices are strongly dependent on load and gas prices, the authors constructed a model for electricity prices based on the effects of these two factors; and used the Geometric Mean Reversion Brownian Motion (GMRBM) model to describe the electricity load process, and a Geometric Brownian Motion(GBM) model to describe the gas prices; deduced the price stochastic process model based on the above load model and gas price model. This paper also presents methods for parameters estimation, and proposes some methods to solve the model.

SVR-Boosting ensemble model for electricity price forecasting in electric power market

A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boosting) is presented in this paper for electricity price forecasting in electric power market. In the light of characteristics of electricity price sequence, a new triangular-shaped 为oss function is constructed in the training of the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accuracy, and possess comparatively satisfactory generalization capability.

Electricity Price Forecasting Based on AOSVR and Outlier Detection

Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, mis paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market

China hikes non-residential electricity price nationwide

On Wednesday, China announced adjustments for the prices of non-residential power and thermal coal in order to ease power shortages and reduce financial pressure on power companies. The National Development and Reform Commission (NDRC) announced that it will raise the retail price

Double Pressure on Power Grids——The demand for electricity is dropping while the electricity price is not adjusted properly

Since October 2008,China's social consumption of electricity had,for the first time,grown negatively compared to the same period of the previous year,and in November the negative growth range further expanded. The major pressure faced by the electricity industry has now turned from the contradiction between coal and electricity to electricity quantity. This is undoubtedly a true and new test to electricity enterprises which get used to high growth but are now suffering great losses. The reform of electricity system has already been in great difficulties and now is getting into a more serious situation. In order to help readers improve their knowledge and understanding of the current tough situation faced by the electricity industry and discuss how to alleviate and get through the difficulty resulted from the economic crisis "encountered once every one hundred years" by joint efforts of all parties concerned,a Seminar on Crisis and Countermeasures for Electricity Industry was held on November 20,2008. Here are some extracts from the speeches of four experts.

Suggestions on the Current Electricity Price Level and Policies in China

The State Council decided to raise the retail electricity price by 0.25 Yuan/kWh from July, 2008. This will, to some extent, relieve the conflicts between power supply and demand, and decrease the economic losses in

Electricity price forecasting using generalized regression neural network based on principal components analysis

A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

Implications of Israeli Agricultural Water Price Sharing System to China

This paper introduces Israeli agricultural water price sharing system. According to Israeli agricultural water cost composition,water price sharing by farmers as well as government subsidy and its forms,the financial subsidy-based agricultural water price system has been established on the basis of the farmers' income in our country and reasonable water price sharing,thus to promote the development of water-saving agriculture in China.

Analysis of China's Electricity Price and Electricity Burden of Basic Industries under the Carbon Peak Target before 2030

The Chinese government is deepening reformation of electricity prices during the 14th Five Year Plan period and has set a carbon emission reduction target of reaching carbon peak before 2030.In this context,will the carbon emission target influence electricity pricing and will electricity price influence competitiveness of Chinese main industries are two questions needing to be answered.This paper compares China's electricity price level with the selected major countries in the world,and four typical industries are selected to evaluate their electricity burden respectively.Then,the correlation between residential electricity price and industrial electricity price and the influencing factors is analyzed,from the perspectives of scale,structure and technology.According to the model obtained by regression analysis,the electricity price level and corresponding residential and industrial electricity burden in 2025 and 2030 are forecasted.Index Terms-Electricity burden,industrial electricity price,regression analysis,residential electricity price.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.

Design of Real-time Electricity Prices and Wireless Communication Smart Meter

Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.

The Impact of Water Price on the Financial Sustainability of the Palestinian Water Service Providers

The impactof water sales price on the performance of water service providers is typically something of a mystery. High prices mean more revenue and profit;but it may lead to less bills collection and encourage the illegal connections. Yet, this argument has not been fully addressed in the Palestinian water sector;this research evaluates the effect of average water prices on the financial sustainability key indicators as collection efficiency, profit or loss percentage, non-revenue water, staff productivity, daily consumption, operating, and maintenance cost. The average price of cubic meter sold is segmented into low, medium, and high categories. Multivariate analysis shows that there are significant differences in profit or working ratio, daily consumption, and operating cost based on the different price categories. Further significant differences have been found in non-revenue water, collection efficiency, and water production based on low and high price categories. On the other hand, no significant difference has been found in staff productivity. The results show high price set by Palestinian water providers, leads to an increase in the bill collection rate and profit margin. However, negative relationship has been found between the price on one hand, and non-revenue water, average daily consumption, and water production on the other hand. The implication of these findings reveal that the Palestinian water providers should increase water prices gradually to cover operating and maintenance cost for better financial performance and sustainability.