Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI).
T he success of proportional-integral-derivative (PID) control in the process industry is based on the ability to stabilize and control around 90% of existing processes [1]. This importance is overshadowed, however, by a lack of performance in some applications. It has been reported that a significant percentage of installed PIDs are operated in manual mode and that 65% of the loops operating in automatic mode generate greater variance in closed-loop operation than in open-loop operation [1], [2]. This lack of performance is, in many cases, the result of a poorly tuned set of parameters due to » lack of knowledge among operators and commissioning personnel » generic tuning methods based on ad hoc criteria that do not match specific process needs » the large variety of PID structures, which leads to errors during application of tuning rules. These challenges motivated our development of the software package Robust Advanced PID Control (RaPID) for tuning PID controllers. RaPID is an intuitive tool with multiple levels of complexity that can be accessed according to the knowledge of the person commissioning the loop. This article describes the methods and algorithms used by RaPID for tuning PID loops. PROJECT DESCRIPTIONIn the project description, the user provides information about the control loop, such as sampling time, ranges, units, names, and descriptions of the setpoints, the controlled variable, and the manipulated variable. These definitions are needed to interpret sampled data and define the limits of variables. The limits provide saturation constraints as well as appropriate scaling of variables.In this phase of the project, the user defines the objective that must be achieved once the PID is tuned (either disturbance rejection or setpoint tracking) as well as the template of the controller. The template of the controller contains the parameter format, which can be selected for several different commercial PID controllers as stand-alone units or integrated in distributed control system (DCS) units. The templates are based on the description provided by the manufacturers, which include Siemens, Emerson, Omron, Honeywell, and others. The templates provide two benefits: 1) exact knowledge of the controller structure to maximize the performance of the controller hardware and 2) elimination of the need for manual conversion of the controller parameters from the traditional Kp, Ti, and Td to the manufacturer's format. This conversion reduces errors due to scaling and entering parameters.The interface also allows the user to employ engineering units rather than scaled values.
In the previous chapter we presented a discussion of the approximation capabilities of fuzzy models. In summary, we have shown that fuzzy models can be used to reproduce the behavior of any continuous function. This chapter presents some of the methods used to construct fuzzy models that replicate the behavior of a given function. The information about the function is presented in the form of input-output data, which means that a set of points over the domain of the function (input) is selected and then evaluated in the function (output).The construction of fuzzy models involves the selection of several parameters: position, shape and the distribution of the membership functions, rule base construction, selection of the logical operations, consequences of the rules, etc. This large number of "degrees of freedom" makes it very difficult to implement a unique method to select all these parameters at once. A typical approach is to set in advance the logical operations and the type of membership functions using certain criteria (differentiability, linguistic integrity, implementability, etc.). The remaining parameters can be estimated from the data using different strategies, but in general all are based on a single objective, which is to minimize the approximation error between the output values and the values given by the fuzzy model. According to the tuned parameters and the strategies, different methods have been proposed in the literature. This chapter presents the following strategies:Mosaic or table lookup scheme [18] • Using gradient descent [18] [19] • Using clustering and gradient descent [12] [4] • Using evolutionary strategies [20] [21]
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