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.

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.

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.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.

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

Alternative techniques for forecasting mineral commodity prices

Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.

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.

Major Determinants of Prices Increase of Building Materials on Ghanaian Construction Market

Prices increase of building materials is a common trend in both developed and developing countries. The prices increase of building materials results in high cost of housing.The aim of this study is to identify the major determinants of prices increase of building materials on Ghanaian construction market, and also to assess the relationship between the independent variables of the prices increase. A five-point Likert scale was used for the study;from strongly disagree (1) to strongly agree (5). The variables in the questionnaire were ranked based on the response of the participants of the study using Mean Response Analysis (MRA) statistics. Spearman correlation matrix was used to determine the relationship between the variables of prices increase of building materials. Crude oil prices, energy cost, local taxes and charges, cost of fuel and power supply, high running cost, high prices of raw materials, cost of transportation and the high cost of labour were found to be the major determinants of prices increase of building materials on Ghanaian construction market. The study further found multicollinearity relationship among variables of prices increase of building materials, of which the highest correlation coefficient was found between fast-growing demand due to high global economic growth and over-dependence on imported building materials. The study recommends that further research should be carried out to determine the control measures of increase prices of building materials in Ghana.

Reactive Power and FACTS Cost Models’ Impact on Nodal Pricing in Hybrid Electricity Markets

In a competitive environment reactive power management is an essential service provided by independent system operator taking into account the voltage security and transmission losses. The system operator adopts a transparent and non-dis-criminatory procedure to procure the reactive power supply for optimal deployment in the system. Since generators’ are the main source of reactive power generation and the cost of the reactive power should be considered for their noticeable impact on both real and reactive power marginal prices. In this paper, a method based on marginal cost theory is presented for locational marginal prices calculation for real and reactive power considering different reactive power cost models of generators’ reactive support. With the presence of FACTS controllers in the system for more flexible operation, their impact on nodal prices can not be ignored for wheeling cost determination and has also to be considered taking their cost function into account. The results have been obtained for hybrid electricity market model and results have also been computed for pool model for comparison. Mixed Integer Non-linear programming (MINLP) approach has been formulated for solving the complex problem with MATLAB and GAMS interfacing. The proposed approach has been tested on IEEE 24-bus Reliability Test System (RTS).

Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Non-convexity Pricing and Allocating Costs in Stochastic Electricity Markets

Stochastic electricity markets have drawn attention due to fast increase of renewable penetrations.This results in two issues:one is to reduce uplift payments arising from non-convexity under renewable uncertainties,and the other one is to allocate reserve costs based on renewable uncertainties.To resolve the first issue,a convex hull pricing method for stochastic electricity markets is proposed.The dual variables of system-wide constraints in a chance-constrained unit commitment model are shown to reduce expected uplift payments,together with developing a linear program to efficiently calculate such prices.To resolve the second issue,an allocation method is proposed to allocate reserve costs to each renewable power plant by explicitly investigating how renewable uncertainties of each renewable power plant affect reserve costs.The proposed methods are validated in a 24-period 3-unit test example and a 24-period 48-unit utility example.

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.

Using Quantile Regression Approach to Analyze Price Movements of Agricultural Products in China

This paper studies how the price movements of pork,chicken and egg respond to those of related cost factors in short terms in Chinese market.We employ a linear quantile approach not only to explore potential data heteroscedasticity but also to generate confidence bands for the purpose of price stability study.We then evaluate our models by comparing the prediction intervals generated from the quantile regression models with in-sample and out-of-sample forecasts.Using monthly data from January 2000 to October 2010,we observed these findings：（i） the price changes of cost factors asymmetrically and unequally influence those of the livestock across different quantiles;（ii） the performance of our models is robust and consistent for both in-sample and out-of-sample forecasts;（iii） the confidence intervals generated from 0.05th and 0.95th quantile regression models are good methods to forecast livestock price fluctuation.

Allocation of congestion relief cost based on flow change on the congested facilities

Congestion management in an electricity market is introduced in this paper and a new method of allocating congestion cost to transactions is proposed. The proposed method is a two-step process, in which the total congestion cost is firstly allocated to congested facilities and then to each transaction involved. The cost of relieving a congested facility allocated to each transaction is proportional to the power flow change on the congested facility caused by the transaction. The more the power flow change is on the congested facility caused by the transaction, the deeper the degree of involvement by the transaction. Therefore, cutting down the magnitudes of such transactions contributes to relieving congestion. Test results on a 5-bus system indicate that the proposed method can reflect reasonably the degree of involvement by each transaction in the congestion and provide correct price signals contributing to relieving congestion.

Transmission pricing and recovery of investment costs in the deregulated power system based on optimal circuit prices

Transmission pricing has become a major issue in the discussions about the deregulated electricity markets. Consequently, open access to the transmission system is one of the basic topics to allow competition among participants in the energy market. Transmission costs have an important impact on relative competition among participants in the energy market as well as on short- and long-term economic efficiencies of the whole electricity industry, although they represent only close to 10% of the energy market price. This paper deals with the design and tests of a transmission pricing method based on the optimal circuit prices derived from the economically adapted network （EAN）. Prices derived from the EAN have the advantage of being in tune with the maximum revenue allowed to the owner of transmission assets and simplifying the optimal allocation of transmission costs among participants. Beginning from the conceptual design, the proposed method is tested on a three-bus network and on the IEEE 24-bus reliability test system.

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

In the electricity market environment,electricity price forecasting plays an essential role in the decision-making process of a power generation company,especially in developing the optimal bidding strategy for maximizing revenues.Hence,it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm.Given this background,this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted Knearest neighborhood(WKNN)method and the Gaussian process regression(GPR)approach.In the first step,several predictors,i.e.,operation indicators,are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators.In the second step,the outputs of the first step are regarded as a new predictor,and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach.The proposed algorithm is verified by actual market data in Pennsylvania-New JerseyMaryland Interconnection(PJM),and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm.Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

Short-Term Electricity Price Forecasting Using Random Forest Model with Parameters Tuned by Grey Wolf Algorithm Optimization

Accurately forecasting short-term electricity prices is of great significance to electricity market participants.Compared with the time series forecasting methods,machine learning forecasting methods can consider more external factors.The forecasting accuracy of machine learning models is greatly affected by the parameters,meanwhile,the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting.Therefore,this paper proposes a random forest(RF)electricity price forecasting model based on the grey wolf optimizer(GWO)to improve the accuracy of forecasting.Among them,RF has a good ability to deal with the problem of non-linear and unstable electricity prices.The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters.On this basis,the short-term electricity prices of the PJM power market in four seasons are separately predicted.Experimental results show that the RF algorithm can better predict the short-term electricity price,and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks （ANN） and weighted least square （WLS） technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices （LMP） in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach pro- posed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland （PJM） interconnec- tion is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

关于进一步完善LNG储气调峰成本疏导机制的探讨——以四川省雅安市为例

随着天然气用气高峰时期气源短缺问题的日益凸显,储气调峰技术已成为缓解用气高峰的通用措施,合理地选择储气调峰方式是保障供气安全平稳进行的首要前提。为了更好地对LNG储气调峰项目进行成本疏导,在分析国内外研究现状的基础上,以四川省雅安市某项目为案例,对项目进行了效益测算,并针对其经济指标不达标的问题对项目进行了优化与改进。通过燃气企业储气调峰服务定价问卷调查,分析燃气企业储气调峰服务价格的可承受能力,将问卷调查与实例计算结合,得出燃气企业LNG储气调峰成本的推荐分摊比例,最后根据计算结果对政府有关部门、燃气企业、终端用户针对性地提出三种成本疏导方案。研究结果表明:①对政府而言,需要合理区分基本与非基本需求,统筹兼顾上下游行业、企业发展和民生保障、经济效益和社会公平公正,做好民生兜底工作,完善困难群体补贴政策,确保其基本生活用气不因价格联动等政策的实施而受影响;②对燃气企业而言,通过与终端用户合理分配LNG储气调峰成本,能够保证其合理利润,也体现了其作为保供主体的责任与优先“保民生”的社会属性;③对终端用户而言,充分考虑困难家庭经济承受能力,在尽可能降低居民支出的同时能够满足其用气高峰时段、季节的正常生产,最终实现多方互赢。结论认为:本研究所得的燃气企业LNG储气调峰成本的推荐分摊比例可为类似项目提供参考。

我国蔬菜产业市场运行态势研究

近10年来,我国蔬菜种植面积、产量逐年增加,消费需求也明显上升,总供给和总需求基本平衡,市场运行总体比较稳健。蔬菜市场运行具有季节性波动、产地转换等五大特征。2023年,我国蔬菜市场产销两旺,市场价格高位运行,农业农村部重点监测的28种蔬菜全国批发价格全年平均是近10年来的最高价;展望2024年,蔬菜总供给和总需求基本平衡,略有结余。蔬菜市场主要面临气候变化、种植意愿、产销衔接等五大风险点。建议今后要进一步强化“菜篮子”建设、蔬菜地产地销、均衡上市、监测预警和政策扶持,努力实现蔬菜产业保供稳价和高质量发展。