Genco's Profit Research Articles

Optimal Mileage Based AGC Dispatch of a GenCo

This paper presents a new real-time optimal mileage based dispatch (OMD) algorithm for a generating company (GenCo) responding to the automatic generation control (AGC) dispatch signals in real time in context of a performance-based frequency regulation market. Distinguished from traditional methods, the proposed method innovatively incorporates the mileage based regulation service compensation criterion in dispatching multiple units to maximize the Genco's total profit. In addition, the proposed method adopts the model predictive control (MPC) framework to effectively address the real-time OMD given dynamic variations of AGC signals between adjacent dispatch intervals to further improve GenCo's profit. The effectiveness of the proposed method is thoroughly tested using the Hainan power grid as part of China Southern Power Grid (CSG) considering various operation scenarios, where a performance-based frequency regulation market is under implementation.

Application of information-gap decision theory to risk-constrained self-scheduling of GenCos

In a competitive electricity market, a generation company (GenCo) optimizes its operation schedules, referred to as self-scheduling, in order to maximize its profit. However, various sources of uncertainty, such as market price fluctuations or forced outage of generating units, may impact the GenCo's profit. In this paper, a non-probabilistic information-gap model is proposed to model the uncertainties in short-term scheduling of a GenCo. The self-scheduling problem is formulated for risk-neutral, risk-averse, and risk-seeker GenCos. Robustness of the decisions against low market prices are evaluated using a robustness model. Furthermore, windfall higher profit due to unpredicted higher market prices is modeled using an opportunity function. The proposed models are applied to a 54-unit thermal GenCo.

Optimal generation scheduling strategy for profit maximization of genco in deregulated power system

A GA based optimal generation scheduling strategy is developed to solve the profit maximization problem for GENCO in the day ahead market. It is a multi objective formulation that includes the forecasted demand, forecasted market clearing price, startup cost and profit functions, besides the generating limits and demand constraints. The proposed GA approach is investigated on three unit and ten unit test systems and numerical results are tabulated. This results shows that this method effectively maximize the GENCO's profit and compared with that of a traditional methods.

Impact analysis of China's coal-electricity price linkage mechanism: Results from a game model

The mechanism of coal-electricity price linkage is a key issue in electricity and coal market reform in China. In this paper we study the impact of the coal-electricity price linkage mechanism on the profit margin of the Chinese power generation company GENCO based on a game model. The results indicate that implementing a coal-electricity price linkage mechanism can reduce the decrease in GENCO's profit margin, and the decrease in GENCO's profit margin mounts up in line with increase in GENCO's market share. We obtain game equilibrium under the two strategies of with and without price linkage and analyze the results using a numerical example.

Managing Price Risk in a Multimarket Environment

In a competitive electricity market, a generation company (Genco) can manage its trading risk through trading electricity among multiple markets such as spot markets and contract markets. The question is how to decide the trading proportion of each market in order to maximize the Genco's profit and minimize the associated risk. Based on the mean-variance portfolio theory, this paper proposes a sequential optimization approach to electric energy allocation between spot and contract markets, taking into consideration the risks of electricity price, congestion charge, and fuel price. Especially, the impact of the fuel market on electric energy allocation is analyzed and simulated with historical data in respect of the electricity market and other fuel markets in the U.S. Simulation results confirm that the proposed analytic approach is consistent with intuition and therefore reasonable and feasible for a Genco to make a trading plan involving risks in an electricity market.

Fuzzy Self-Scheduling for GenCos

This letter presents a comprehensive fuzzy approach for maximizing a GenCo's profit in a competitive electricity market considering uncertainties. The market uncertainties considered are the demand, spinning and nonspinning reserves, market prices, and probability that reserves are called and generated. The proposed technique produces an optimal profit fuzzy function that reflects the market uncertainties and helps for hedging risks.

Security-Constrained Optimal Generation Scheduling for GENCOs

This paper presents an approach for maximizing a GENCO's profit in a constrained power market. The proposed approach considers the Interior Point Method (IPM) and Benders decomposition for solving the security-constrained optimal generation scheduling (SC-GS) problem. The master problem represents the economic dispatch problem for a GENCO which intends to optimize its profit. The formulation of the master problem does not bear any transmission network constraints. The subproblem will be used by the same GENCO to check the viability of its proposed bidding strategy in the presence of transmission network constraints. In this case if the subproblem does not yield a certain level of financial return for the GENCO or if the subproblem results in an infeasible solution of the GENCO's proposed bidding strategy, the GENCO will modify its proposed solution according to the Benders cuts that stem out of the subproblem. The study shows a more flexible scheduling paradigm for a GENCO in a competitive arena. The proposed approach proves practical for modeling the impact of transmission congestion on a GENCO's expected profit in a competitive environment.