Zuwei Yu

A new long-term hydro production scheduling method for maximizing the profit of hydroelectric systems

This paper presents a new long-term hydrothermal production scheduling method. The proposed method maximizes the profit of hydroelectric plants, based on the monthly energy requirement of the system, instead of minimizing the production cost of thermal units. It is shown that different forms of composite thermal marginal costs will lead to the same hydro production schedule. Thus a linear marginal cost, the simplest form, is sufficient for long-term hydrothermal scheduling. A linear hydro marginal profit is also sufficient for this purpose. An immediate conclusion is that an actual composite thermal cost function, which is complicated by thermal unit availability, may not be needed for the long-term optimal hydrothermal scheduling. Due to this simplification, traditional long and mid-term hydrothermal scheduling, a complicated problem, becomes easier to solve. The method can be used by the owners of independent hydro plants in a region for long-term hydroelectric scheduling under both deregulation and competition. A case study shows that the model allocates successfully and efficiently the hydroelectric resources to peak demand periods with negligible computation time.

Wind speed modeling and energy production simulation with Weibull sampling

This paper describes the maximum likely estimation (MLE) method and the method of moments (MOM) for wind speed modeling. The Weibull wind speed distribution models are fitted using the two methods and the wind data from a tall tower in the Midwestern United States, with seasonal wind speed variations also considered in the modeling. It turned out that both methods provide very similar results with comparable accuracy. The Monte Carlo simulation is used for obtaining expected wind energy production using a Weibull sampling technique.

Modeling electricity trade policy for the twelve nations of the Southern African Power Pool (SAPP)

Abstract The benefits from a centralized and competitive dispatch, compared with existing bilateral electricity trade agreements are determined to be about

Fractional weibull wind speed modeling for wind power production estimation

100 million per year in the 12-nation Southern African Power Pool (SAPP). Generation, transmission, and costing variables are uniquely incorporated into a short-term cost minimizing mixed integer linear programming model, to determine the optimal unit commitment and dispatch across this 12-nation pool of the Southern African Development Community (SADC) region of Africa. Costs are minimized from increasing the use of existing hydropower facilities in the Democratic Republic of Congo (DRC), Zambia, and Mozambique and reducing fuel costs primarily in South Africa.

A theoretical analysis on parameter estimation for the weibull wind speed distribution

This paper describes the method of the Fractional Weibull Distribution (FWD) for modeling wind speed distributions. The Maximum Likelihood Estimation (MLE) method is used for estimating the parameters of the FWD, fitted to the wind data from a tall tower. Seasonal wind speed variations are considered in the modeling. Compared to the standard Weibull distribution estimates, the FWD estimates yield greater accuracy in wind power estimation. The discrete distributions of the probabilities of the FWDs are used for obtaining expected wind energy production, which shows a considerable reduction in errors by about tenfold. A simple Mean-Variance analysis of power production is performed for a wind farm that can have a mix of three different turbine models. The results indicate that the standard deviation of power production can be considerably reduced by choosing an appropriate mix of turbines.

A large oligopoly competition model with an application to the Midwest electricity markets

The Weibull distribution has been found to be a suitable fit for the empirical distribution of the wind speed measurement samples. We discuss the application of the Maximum Likelihood Estimation (MLE) method and the Method of Moments (MOM) for fitting the 2-parameter Weibull distribution to wind speed measurements. We also prove that both the MLE and MOM yields unique parameter estimates.

An enhanced model of capacity expansion with CO 2 cap-and-trade and EOR CO 2 re-emergence

This paper presents a large spatial gaming model with price caps for the Midwest electricity markets. Price caps are enforced in several deregulated regional electricity markets in the USA, a logical step is to reflect this reality in imperfect competition modeling. Unfortunately, most current gaming models have not included any price cap formulation. This paper is one of the first to address the issue. A transportation formulation is used for representing the spatial nature of an electrical network. An algorithm is proposed to find a Nash equilibrium under the enforcement of price caps based on the Kuhn-Tucker vector optimization theorem. Case studies show the successful application of the model. The conclusion is that, given appropriate price caps, market power impact can be reduced in the short run.

Integrated risk management under deregulation

This paper describes an enhanced model of power production under CO2 cap-and-trade regulation. Re-emergence of CO2 from EOR (enhanced oil recovery) processing is considered, and the net CO2 release is quantified accordingly. New cost data is used to reflect the cost increase in the past few years. Binary variables are used for new construction of power plants, transmission, CO2 pipelines, and retrofitting, while continuous variables are used for representing others such as power production, CO2 generation, etc. The objective is to minimize regional cost of meeting demand under potential CO2 cap-and-trade regulation in the United States. In case studies, 28 nodes are used to represent production and demand in five States including Indiana and its surrounding states. Seventy sequestration sites are chosen for the 28 nodes, including EOR, ECBM (enhanced coal bed methane) and aquifers.

Planning wind power considering system reliability in peak demand time

The paper draws lessons from the California power crisis and the recent fall of Enron and promotes integrated risk management. A general framework for integrated risk management is proposed, with the major risks listed for the deregulated energy industry. The risks are categorized into five classes-people, business, finance, shocks and information related risks. A general procedure and two risk assessment methods are also introduced for integrated risk management.

Fractional Weibull wind speed modeling for wind power production estimation

This paper describes an integrated power capacity planing model that considers the contribution of wind power to system reliability in peak demand periods. Seasonal expected energy production of wind power is used to account for the reliability contribution of wind power. In order to minimize expansion cost over a long term planning horizon, primary alternatives of power expansion are included. Mixed integer programming (MIP) is used in the modeling. Case studies are presented to show that a region with lower wind speed in peak time requires a significant amount of peaking generators to compensate for low wind power availability in order to meet the required reserve.