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.

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.

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.

Annual Report of SGCC on Transactions of Electric Power Market in 2009

Market construction Overview In 2009, the electric power market expanded continuously. New installed capacity put into production within the coverage of the State Grid Corporation

Pricing Mechanism in Power Market Construction

The reform on electricity pricing mechanism is a critical problem in power market construction in China, and is in mutual supplementation and promotion with the latter. In particular, the pricing mechanism for electricity fed into network and that for electricity transmission and distribution as well as the relationship between coal and electricity prices, etc. have to be studied in depth. This paper presents several solutions and suggestions to these problems.

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.

Bidding Strategy in Deregulated Power Market Using Differential Evolution Algorithm

The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).

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 (fnel) prices is analyzed in this paper. Because electricity prices are strongly depen-dent 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 new method on hydrothermal scheduling optimization in electric power market

The hydrothermal scheduling in the electric power market becomes difficult because of introducing competition and considering sorts of constraints. An augmented Lagrangian approach is adopted to solve the problem,which adds to the standard Lagrangian function a quadratic penalty term without changing its dual property,and reduces the oscillation in iterations. According to the theory of large system coordination and decomposition,the problem is divided into hydro sub-problem and thermal sub-problem,which are coordinated by updating the Lagrangian multipliers,then the optimal solution is obtained. Our results for a test system show that the augmented Lagrangian approach can make the problem converge into the optimal solution quickly.

Optimal operation of cascaded hydroelectric power plants in the power market

An improved network flow algorithm, which includes the minimum cost network flow and the same period network flow, is proposed to solve the optimization of cascaded hydroelectric power plants in a competitive electricity market. The typical network flow is used to find the feasible flow and add the discharge water to different cascaded hydroelectric power plants at the same step. The same period network flow is used to find the optimal flow and add the power output at a different step. This new algorithm retains the advantages of the typical network flow, such as simplicity and ease of realization. The result of the case analysis indicates that the new algorithm can achieve high calculation precision and can be used to calculate the optimal operation of cascaded hydroelectric power plants.

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, this 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.

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.

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.

Understanding electricity prices beyond the merit order principle using explainable AI

Electricity prices in liberalized markets are determined by the supply and demand for electric power,which are in turn driven by various external influences that vary strongly in time.In perfect competition,the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load,i.e.the difference of load and renewable generation.Various market models are based on this principle when attempting to predict electricity prices,yet the principle is fraught with assumptions and simplifications and thus is limited in accurately predicting prices.In this article,we present an explainable machine learning model for the electricity prices on the German day-ahead market which foregoes of the aforementioned assumptions of the merit order principle.Our model is designed for an ex-post analysis of prices and builds on various external features.Using SHapley Additive exPlanation(SHAP)values we disentangle the role of the different features and quantify their importance from empiric data,and therein circumvent the limitations inherent to the merit order principle.We show that load,wind and solar generation are the central external features driving prices,as expected,wherein wind generation affects prices more than solar generation.Similarly,fuel prices also highly affect prices,and do so in a nontrivial manner.Moreover,large generation ramps are correlated with high prices due to the limited flexibility of nuclear and lignite plants.Overall,we offer a model that describes the influence of the main drivers of electricity prices in Germany,taking us a step beyond the limited merit order principle in explaining the drivers of electricity prices and their relation to each other.

Electricity Price of Hybrid Power System and Decision Making of Renewable Energy Investment Capacity

In the existing electricity market,the traditional power suppliers and renewable energy generators coexist in the power supply side. In the power supply side,renewable energy generators generate power by wind and other natural conditions,leading renewable energy output a certain randomness. However,the low marginal generating cost and the reduction of carbon emissions,and thus brings a certain advantage for renewable energy compared to alternative energy. Electricity,as a special commodity,stable and adequate power supply is a necessary guarantee for economic and social development. Power shortage situation is not allowed in the power system,and the extra power needs to be handled for the purpose of safety. In this paper,the hybrid power generated by renewable energy generators and traditional energy generators is used as power supply,and then the electricity market sells hybrid power to electricity consumers,the hybrid power system determines the optimal daytimeprice,nighttime price,and the optimal installed capacity of the renewable energy suppliers. We find that the installed capacity of renewable energy increases first and then decreases with the increase of the price sensitivity coefficient of traditional energy supply. Electricity demand is negatively related to electricity price in the current period,and is positively related to price in the other period. The average price of day and night is only related to the total potential demand of day and night and the total generation probability of renewable energy. The price difference between daytime and nighttime is positively related to potential electricity demand,and negatively related to the sensitivity coefficient of electricity price.

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).

Effect of Market Power in Electricity Markets

The main objective of this paper is to show an overview analysis of market power issues.Market power reflects the scarcity of power supply.It is the ability of a particular seller or group of sellers to maintain prices profitably above competitive levels for a significant period of time.Because the electric power system has its own characteristics that are different to other economic systems,both physical factors and economic factors of power system are key elements on this definition.We study some cases here,including different line limit levels,load levels and bid strategy through a market model based on OPF (optimal power flow) with a decommitment algorithm.