Multi-mode operation of Combined Cycle Gas Turbines with increasing wind penetration

Abstract

As power systems evolve to incorporate greater penetrations of variable renewables, the demand for flexibility within the system is increased. Combined Cycle Gas Turbines (CCGTs) are traditionally considered as inflexible units but those which incorporate a steam bypass stack are capable of open-cycle operation. Facilitating these units to operate in open-cycle mode is shown to improve system reliability and reduce emissions. It also yields benefits for the generators themselves via increased revenues (in some circumstances) and reduced cycling. Index Terms-Thermal Power Generation, Wind Power Generation, Power System Modeling. N. Troy (niamh.troy@ucd.ie) and M. O'Malley (mark.omalley@ucd.ie) are with the

A composable method for real-time control of active distribution networks with explicit power setpoints. Part I: Framework

“…First, GA 1 computes the aggregated PQt profile as the set of all possible power flows (P 1 0 , Q 1 0 ) at the PCC of Grid 1 and Grid 0 , given that the powers injected by the followers of GA 1 belong to their respective PQt profiles, advertised to GA 1 ; this is computed by solving the load-flow for Grid 1 . Second, GA 1 computes the value of the aggregated virtual cost function for every (P 1 0 , Q 1 0 ) that is in the aggregated PQt profile as follows: GA 1 applies its decision process in order to obtain a collection of power setpoints for its set of followers and returns the corresponding value of the objective function (6). Last, the value of the aggregated belief function at (P 1 0 , Q 1 0 ) is the set equal to the union of all possible actual power flows at the PCC of Grid 1 and Grid 0 over all possible actual power injections at the followers, given by the belief functions advertised to GA 1 .…”

“…As was already mentioned, for computing the aggregated virtual cost function at a given requested setpoint at the PCC u 0 = (P 0 , Q 0 ), the internal GA applies its gradient descent algorithm (7) in order to obtain a collection of power setpoints u for its set of followers, and it returns the corresponding value of the objective function (6). In this paper, in order to advertize the virtual cost for every u 0 ∈ A * 0 , we compute it on a sparse partition of the aggregated PQt profile A * 0 and advertize a linear interpolation thereof.…”

“…Such an approach, however, has many drawbacks in systems characterized by dominant non-dispatchable stochastic renewable energy resources where, to balance the power, the nondesirable use of traditional power plants (usually gas-fired power plants e.g. [6] or, when available, hydro power plants) is necessary. In contrast, if in distribution networks it is possible to expose to a grid controller the state of each energy resource (i.e., generation, storage, and loads) in a scalable way, then it is possible, in principle, to always find an admissible and stable system-equilibrium point with small or negligible power-balancing support from the external grid.…”

How Does Energy Storage Increase the Efficiency of an Electricity Market with Integrated Wind and Solar Power Generation?—A Case Study of Korea

“…Ramp wear and tear costs are the result of physical stress imposed on power generators due to rapid changes in power output, which are caused by the variability of renewable generation. This cost has been discussed in recent studies, including Troy et al [12], Kumar et al [13], Lamadrid et al [14], and Wogrin et al [15]. The methodology of applying ramp wear and tear costs in this model follows Jeon [16].…”

“…To produce realistic wind power generation profiles, wind speed was estimated using the two-stage ARMAX model (ARIMA (Autoregressive Integrated Moving Average) with exogenous variables), as shown in Equations (10)- (12). In the first stage, which is described in detail in Equation (12), one-year, half-year, one-day, and half-day cycles, as well as cooling (CDD (Cooling degree days index = Max (temperature − 18, 0))) and heating degree days (HDD (Heating degree days index = Max (18 − temperature, 0))) were set as the main explanatory variables for wind speed, so that log(wind speed + 1) (To avoid zero wind speed in log function, one is added) could be estimated using the OLS (Ordinary Least Square). In the second stage, the ARIMA model was estimated, with the residuals (u t ) of the OLS estimation equation set as dependent variables to address autocorrelation.…”