Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.

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.[

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%.

Daily rolling estimation of carbon emission cost of coal-fired units considering long-cycle interactive operation simulation of carbon-electricity market

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets.The carbon emission cost(CEC)of coal-fired units becomes part of the power generation cost through market coupling.The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market.Study of carbon–electricity market interaction and CEC calculations is still in its initial stages.This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets.A long-period interactive operation simulation mechanism for the carbon–electricity market is established,and operation and trading models of the carbon emissions trading market and electric power market are established.A daily rolling estimation method for the CEC of coal-fired units is proposed,along with the CEC per unit electric quantity of the coal-fired units.The feasibility and effectiveness of the proposed method are verified through an example simulation,and the factors influencing the CEC are analyzed.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Investing and Pricing with Supply Uncertainty in Electricity Market:A General View Combining Wholesale and Retail Market

Renewable energy,such as wind and solar energy,may vary signifi cantly over time and locations depending on the weather and the climate conditions.This leads to the supply uncertainty in the electricity(power) market with renewable energy integrated to power grid.In this paper,electricity in the market is classified into two types:stablesupply electricity(SSE) and unstablesupply electricity(USE).We investigate the investment and pricing strategies under the electricity supply uncertainty in wholesale and retail electricity market.In particular,our model combines the wholesale and retail market and capture the dominant players,i.e.,consumers,power plant(power operator),and electricity supplier.To derive the market behaviors of these players,we formulate the market decision problems as a multistage Stackelberg game.By solving the game model,we obtain the optimal,with closedform,wholesale investment and retail pricing strategy for the operator.We also obtain the energy supplier's best price mechanism numerically under certain assumption.We fi nd the price of SSE being about 1.4 times higher than that of USE will benefi t energy supplieroptimally,under which power plant's optimal strategy of investing is to purchase USE about 4.5 times much more than SSE.

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.

Electricity Pricing Policy Should Serve Macro-Economic Control

The National Development and Reform Commission beefed up its efforts to promote the work of electricity price reform in 2004. It took measures in aspects of easing price contradictions, tentatively implementing discriminated price and time-of-use price and issuing a policy on the linkage of coal and electricity prices and price administration. In 2005 the basic thinking of the Commission's electricity price reform is to actively advance the pilot work of price reform,carry out the reform of transmission and distribution price, enlarge the scope of pilot work of large consumer's direct purchase, simplify sales price structure and bring about the linkage of coal and electricity prices.

A New Round of Reform in Electricity Pricing System in China

The State Council issued the Scheme for Reforming Electricity Pricing System m the second half of year 2003. It touches upon separating price of plant from that of network, sales price to network, transmission and distribution price, sales price and system principles in regard to electricity tariff mainly.

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.

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.

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.

Pricing Mechanism of China’s Trans-regional and Trans-provincial Electricity Trading

With the large-scale development of Chinese electric power, the contradiction of China’s energy supply and demand that reverse distributed is very prominent, therefore, promoting electricity trading is one of the important measures to get optimized configuration of energy resources in nationwide. For the two kinds of trading method, the “power point to the grid” trading and the “grid to grid” trading, this paper designed pricing mechanism model, and took one area as an example, we analyzed the impact of the participants by using different pricing mechanism, and put forward reasonable policy proposals for China’s pricing mechanism of trans-regional and trans-provincial electricity trading.

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.

Analysis of Nigerian Electricity Generation Multi Year Tariff Order Pricing Model

The Multi Year Tariff Order (MYTO) is the Nigerian Electricity Regulatory Commission (NERC) pricing framework for determining the Nigerian Electricity Supply Industry (NESI) pricing model. One of the objectives of the NERC’s MYTO pricing model is to ensure regulated electricity end user tariff without compromising return on investment. Achieving this objective is imperative to attract investors in the growing Nigerian electricity market. However, NESI has hitherto been faced with challenges ranging from its inability to provide sufficient power to its customers to not being viable enough to provide return on capital invested. In this paper, sensitivity analysis of power plant operation and performance parameters on the cost of electricity (CoE) generation using MYTO (power generation) pricing model were evaluated. Thermodynamic modeling and simulation of an open cycle gas turbine (OCGT) was carried out to augment scarce data on power plant performance and operation in Nigeria. Sensitivity analysis was carried out using probabilistic method based on Monte Carlo simulation (MCS) implemented in commercial software (@ Risk®). The result highlighted sensitivity of the model input parameters to cost of electricity generation based on technical and financial assumptions of MYTO model. Seven most influential parameters affecting generation cost were identified. These parameters and their correlation coefficients are given as: 1) foreign exchange rate, 0.76;2) cost of fuel, 0.51;3) thermal efficiency, -0.23;4) variable operation and maintenance cost, 0.22;5) fixed operating and maintenance cost, -0.03;6) capacity factor, -0.02;and 7) average capacity degradation, 0.01. Based on the gas turbine engine and input parameter distributions statistics for this study, the generation cost lies between 9.84 to 15.45 N/kWh and the probabilities of CoE within these values were established.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

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.

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models;and 2) Simulation based models;time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.

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.