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In an Electric Submersible Pump (ESP) lifted oil field, the ESP of each oil well should be operated inside its operating window. The total power consumed by the ESPs in the oil field should be minimized. The speed of the ESPs and the production choke valve opening should be optimally chosen for maximizing the total oil produced from the oil field. At the same time, the capacity of the separator should not be exceeded. In this paper, nonlinear steady state optimization based on Sequential Quadratic Programming (SQP) is developed. Two optimal control structures are proposed in this paper. In the first case, the optimal pump speed is controlled by a PI controller by varying the electrical excitation signal to the motors. The optimal fluid flow rate through each oil well is controlled by another PI controller by varying the production choke valve opening. The paper shows that the production choke valve for each oil well has to be always 100% open to maintain the optimal fluid flow rate. In the second case, the production choke valves are considered to be always 100% open as hard constraints. The optimal fluid flow rate through each oil well is controlled by a PI controller by varying the pump speed. It is shown that when the optimal fluid flow rate is tracked by the controller, the speed of each of the pumps is equal to the optimal pump speed calculated by the optimizer. This basically means that we can achieve the optimization objective with the same optimal results as in the first case by using only a single PI controller. The limitations of these two optimal control structures for very low values and for very high values of the separator capacity are discussed. For the feasible range of separator capacities, the optimal locus of the fluid flow rate and the pump speed are shown in this paper.

The ever-increasing penetration of intermittent energy sources introduces new demanding operating regimes for the bulk power generation in the power grid. During a worstcase power system disturbance scenario, the generator needs to operate beyond its limits to maintain stable operation. Therefore, a new online low-order thermal model of a hydrogenerator has been recently proposed, where the periodic extension of the long-forgotten capability diagram of the machine was in-depth investigated. An increased performance can be obtained if the total thermal capacity of the generator is exploited by applying optimal control theory in the Automatic Voltage Regulator (AVR). This paper proposes a Non-linear Model Predictive Controller (NMPC) combined with an Unscented Kalman Filter (UKF) with a modeling framework geared for use in a supervisory control structure for the conventional control system. The method provides maximum utilization of the machine's thermal capacity by providing the controller with an Enhanced Capability Diagram (ECD). Case studies on a single-machine system and a 16-bus multi-machine system were investigated. The results show that a satisfying controller action during different long-term voltage instability scenarios is realized.

Proper allocation and distribution of lift gas is necessary for maximizing total oil production from a eld with gas lifted oil wells. When the supply of the lift gas is limited, the total available gas should be optimally distributed among the oil wells of the eld such that the total production of oil from the eld is maximized. This paper describes a non-linear optimization problem with constraints associated with the optimal distribution of the lift gas. A non-linear objective function is developed using a simple dynamic model of the oil eld where the decision variables represent the lift gas ow rate set points of each oil well of the eld. The lift gas optimization problem is solved using the 'fmincon' solver found in MATLAB. As an alternative and for verication, hill climbing method is utilized for solving the optimization problem. Using both of these methods, it has been shown that after optimization, the total oil production is increased by about 4%. For multiple oil wells sharing lift gas from a common source, a cascade control strategy along with a nonlinear steady state optimizer behaves as a self-optimizing control structure when the total supply of lift gas is assumed to be the only input disturbance present in the process. Simulation results show that repeated optimization performed after the rst time optimization under the presence of the input disturbance has no eect in the total oil production.

Design of optimal operation and control of a run-of-river hydro power plant depends on good models for the elements of the plant. River reaches are often considered to be shallow channels with free surface flow. A typical model for such reaches thus use the Saint Venant model, which is a 1D model based on the mass and momentum balances. This combination of free surface and momentum balance makes the problem numerically challenging to solve. Here, the finite volume method with staggered grid is used to illustrate the dynamics of the river upstream from the Grønvollfoss run-of-river power plant in Telemark, Norway, operated by Skagerak Energi. A model of the same river in the Grønvollfoss power plant has been studied previously, but here the geometry of the river is changed due to new information from Skagerak Energi. The numerical scheme for solving the model has been further developed. In addition, the behavior of the dynamic model is compared to data from experiments, carried out on the Grønvollfoss run-of-river power plant. The essence of the experiments is to consider the time taken from an increase in the input volumetric flow, to a measured change in level in front of the dam at Grønvollfoss. The model is manually tuned by changing the Strickler friction factor, the river length and the type of river slope/width (constant/varying) in order to fit the water level ahead of the Grønvollfoss dam from experimental data. Least squares model fitting is also used for the model with the constant slope and width of the river and this model shows good fitting after the manual tuning. The results of the improved model (numerically, tuned to experiments), is a model that can be further used for control synthesis and analysis.

Abstract-Dynamic and mechanistic models of an Electrical Submersible Pump (ESP) lifted oil field are frequently used for understanding the process dynamics and the interactions occurring between the oil wells under varying operating conditions. They are also used to calculate the total fluid produced from the oil field. In this article, uncertainty and sensitivity analysis of such a model is studied. If the model is used for control and optimization of an oil field, it is important to see if the output calculated using the mathematical model lies within a confidence interval under the presence of uncertainties. It is also important to understand which parameters have strong/weak influence on the model output. The uncertainty analysis is performed by using the Monte Carlo simulation method. Morris method of elementary effect is used for input factor screening. To quantify the effect of the input factors on the model output, a variance based method of sensitivity analysis is used. Index Terms-Model uncertainty, ESP lifted oil field, morris method, variance based sensitivity analysis

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