Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois

Abstract

In this paper, we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and real-time electricity markets, which the system operator clears by centralized unit commitment and economic dispatch. We use probabilistic wind power forecasting to estimate dynamic operating reserve requirements, based on the level of uncertainty in the forecast. At the same time, we represent price responsive demand as a dispatchable resource, which adds flexibility in the system operation. In a case study of the power system in Illinois, we find that both demand dispatch and probabilistic wind power forecasting can contribute to efficient operation of electricity markets with large shares of wind power.

RETRACTED: Active robust optimization for wind integrated power system economic dispatch considering hourly demand response

“…In existing researches [14,15], [25,31,36], the nodal prices are computed by a simple specified function reflecting their relationship with energy consumption quantity without considering the complex formation mechanism based on the power system operation condition. Here, we take the OPF-based nodal prices into account.…”

“…The SO-based approach introduced as early as Dantzig [7] relies on pre-sampling discrete scenarios of the uncertainty realizations [8,9] via presumed probability distribution functions (PDFs) of uncertainty parameters and tries to compute a solution which optimizes the expected value of the objective function. Representative researches include stochastic security-constrained unit commitment (SCUC) [8], [10,11], stochastic ED [12,13], DR dispatch [14,15], stochastic optimal power flow (OPF) [16,17], and stochastic electric vehicle (EV) charging [18,19]. But the SO-based approach still has practical limitations in its application to large scale power systems.…”

A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

“…The distribution of mean wind speeds over long periods (annual energy production) can be approximated by a Weibull distribution [11,[14][15][16][17][18][19]. When evaluating the distribution of wind speeds over a short time period, the wind activity is dominated by turbulence and a Gaussian distribution can be used.…”

“…These models include the stochastic renewable energy and random load behavior. On the other hand, some solution strategies consider: (a) overestimation and underestimation cost of available wind power [14], (b) temporal behavior for wind and demand [15][16][17], (c) multi-objective programming [16], (d) G Model No. of Pages 8 probabilistic constraints [18], (e) market decision-making technique [19], (f) artificial intelligent optimization methods [20,21] and (g) reserve market modeling [22,23]. These methods focus on the mean and variance of power generation.…”

Risk and unit commitment decisions in scenarios of wind power uncertainty

“…The decisions are dynamically adjusted over the time, and scenario trees may be used to facilitate the formulation. The wind power forecast was shown to play an important role in the calculation of the operation reserves in SUC [16,17] In [18], one finds an approach to calculate the operational reserves requirements based on the quantiles from probabilistic wind power forecast. Take in consideration the probabilistic nature of wind power.…”