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

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.

Equilibria in Interdependent Natural-gas and Electric Power Markets: an Analytical Approach

Natural-gas and electric power systems and their corresponding markets have evolved over time independently. However, both systems are increasingly interdependent since combined cycle gas turbines that use natural gas to produce electricity increasingly couple them together. Therefore, suitable analysis techniques are most needed to comprehend the consequences on market outcomes of an increasing level of integration of both systems. There is a vast literature on integrated natural-gas and electric power markets assuming that the two markets are operated centrally by a single operator. This assumption is often untrue in the real world, which necessitates developing models for these interdependent yet independent markets. In this vein, this paper addresses the gap in the literature and provides analytical Nash-Cournot equilibrium models to represent the joint operation of natural-gas and electric power markets with the assumption that the market participants in each market make their own decisions independently seeking the maximum profits, as often is the case in the real world. We develop an analytical equilibrium model and apply the Karush-Kuhn-Tucker (KKT) approach to obtain Nash-Cournot equilibria for the interdependent natural-gas and electric power markets. We use a double-duopoly case to study the interaction of both markets and to derive insightful analytical results. Moreover, we derive closed-form analytical expressions for spot-market equilibria in both natural-gas and electric power markets, which are relevant and of practical significance for decision makers. We complement the double-duopoly study with a detailed sensitivity analysis.

Stability Analysis of Sampled-data Control System and Its Application to Electric Power Market

The stability of sampled-data systems is investigated using a new type of Lyapunov functional.The interval from the sampling point t_(k) to t_(k+1) is assumed to be a sampling interval.By fully utilizing the characteristic information on the whole sampling interval,a new two-sided closed-loop Lyapunov functional is proposed,which utilizes the information on both the intervals from the sampling point t to k_(t) and from t to t_(k+1).Based on the two-sided closed-loop Lyapunov functional and modified free-matrix-based inequality,a less conservative stability criterion is derived for a sampled-data control system,and three numerical examples are provided to verify the effectiveness and reduced conservativeness of the proposed method.Furthermore,the proposed method is applied to solve the stability problem of electric power markets,and the practical significance of reducing the conservativeness is discussed.

Coopetition Model： Evidence from Electric Power Market in the Southwest of China

To be a necessity for life issue as well as an important engine to country＇s economy, electricity has private and public goods properties simultaneously, leading it difficult to balance economic profit and social responsibility for a single state-owned cor- poration especially in large rural areas. In this paper, we suggest that occurrence of local company may provide us a new insight for this issue. Using objective programming model, we discuss different outcomes under competitive and cooperative situations. Our results suggest coopetitive relations among state company and local company would be a practical choice for achieving the economic profit and social duty simultaneously. Furthermore, tak- ing Sichuan province as a case, we investigate three types of coopetition relation between state company and local companies. Our findings suggest that coopetition strategy may lead to better solution to dealing with the conflicts between economic profit and social responsibility for both state-owned corporation and local electric power company.

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