Probabilistic Unit Commitment under a Deregulated Market
Jorge F. Valenzuela, Mainak Mazumdar
Abstract
Jorge F. Valenzuela, Mainak Mazumdar
Abstract
In this paper, we propose a new formulation of the unit commitment problem that is suitable for the deregulated electricity market. Under these conditions, an electric power generation company will have the option to buy or sell from a power pool in addition to producing electricity on its own. We express the unit commitment problem as a stochastic optimization problem in which the objective is to maximize expected profits and the decisions are required to meet the standard operating constraints. Under the assumption of competitive market and price-taking, we show that the unit commitment problem for a collection of M generation units can be solved by considering each unit separately. The volatility of the spot market price of electricity is represented by a stochastic model. We use probabilistic dynamic programming to solve the stochastic optimization problem pertaining to unit commitment. We show that for a market of 150 units the proposed unit commitment can be accurately solved in a reasonable time by using the normal, Edgeworth, or Monte Carlo approximation methods.
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In this paper, we propose a new formulation of the unit commitment problem that is suitable for the deregulated electricity market. Under these conditions, an electric power generation company will have the option to buy or sell from a power pool in addition to producing electricity on its own. We express the unit commitment problem as a stochastic optimization problem in which the objective is to maximize expected profits and the decisions are required to meet the standard operating constraints. Under the assumption of competitive market and price-taking, we show that the unit commitment problem for a collection of M generation units can be solved by considering each unit separately. The volatility of the spot market price of electricity is represented by a stochastic model. We use probabilistic dynamic programming to solve the stochastic optimization problem pertaining to unit commitment. We show that for a market of 150 units the proposed unit commitment can be accurately solved in a reasonable time by using the normal, Edgeworth, or Monte Carlo approximation methods.
Key concepts: Power system simulation, Probabilistic logic, Electricity market, Mathematical optimization, Unit (ring theory), Electricity, Stochastic programming, Computer science