scholarly journals Dynamic Optimization of a Subcritical Steam Power Plant under Time-Varying Power Load

2018 ◽  
Author(s):  
Chen Chen ◽  
George M. Bollas
Keyword(s):  
Power Plant ◽  
Dynamic Optimization ◽  
Electricity Market ◽  
Optimization Problems ◽  
Optimal Time ◽  
Time Varying ◽  
Power Demand ◽  
Test Bed ◽  
Simulation And Optimization ◽  
Power Load

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

scholarly journals Dynamic Optimization of a Subcritical Steam Power Plant Under Time-Varying Power Load

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 114 ◽  
Author(s):  
Chen Chen ◽  
George Bollas
Keyword(s):  
Power Plant ◽  
Dynamic Optimization ◽  
Electricity Market ◽  
Optimization Problems ◽  
Optimal Time ◽  
Time Varying ◽  
Power Demand ◽  
Test Bed ◽  
Simulation And Optimization ◽  
Power Load

The increasing variability in power plant load in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort and are the subjects of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model was used as a test-bed for dynamic simulations, in which the coal load was regulated to satisfy a varying power demand. Plant-level control regulated the plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant’s operation at varying loads was optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems were formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points was shown to be feasible with corresponding savings in coal consumption of 184.8 tons/day and a carbon footprint decrease of 0.035 kg/kWh.

Introduction to dynamic programming

2018 ◽  
Author(s):  
Hans Fehr ◽  
Fabian Kindermann
Keyword(s):  
Dynamic Programming ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Optimal Time ◽  
Programming Technique ◽  
Solution Algorithms ◽  
Deterministic Dynamic ◽  
Specific Objective ◽  
Programming Techniques ◽  
Time Discount

Dynamic optimization is widely used in many fields of economics, finance, and business management. Typically one searches for the optimal time path of one or several variables that maximizes the value of a specific objective function given certain constraints. While there exist some analytical solutions to deterministic dynamic optimization problems, things become much more complicated as soon as the environment in which we are searching for optimal decisions becomes uncertain. In such cases researchers typically rely on the technique of dynamic programming. This chapter introduces the principles of dynamic programming and provides a couple of solution algorithms that differ in accuracy, speed, and applicability. Chapters 8 to 11 show how to apply these dynamic programming techniques to various problems in macroeconomics and finance. To get things started we want to lay out the basic idea of dynamic programming and introduce the language that is typically used to describe it. The easiest way to do this is with a very simple example that we can solve both ‘by hand’ and with the dynamic programming technique. Let’s assume an agent owns a certain resource (say a cake or a mine) which has the size a0. In every period t = 0, 1, 2, . . . ,∞ the agent can decide how much to extract from this resource and consume, i.e. how much of the cake to eat or how many resources to extract from the mine.We denote his consumption in period t as ct. At each point in time the agent derives some utility from consumption which we express by the so-called instantaneous utility function u(ct). We furthermore assume that the agent’s utility is additively separable over time and that the agent is impatient, meaning that he derives more utility from consuming in period t than in any later period.We describe the extent of his impatience with the time discount factor 0 < β < 1.

Reliability Index of Wind-Solar Hybrid Power Plants using the Expected Energy Not Supplied Method

2019 ◽  
Vol 8 (4) ◽  
pp. 9449-9456
Keyword(s):  
Power Plant ◽  
Power Systems ◽  
Electricity Market ◽  
Reliability Index ◽  
Power Plants ◽  
Initial Conditions ◽  
Hybrid Plant ◽  
Total Power ◽  
Hybrid Power ◽  
Hybrid Power Plant

This paper proposes the reliability index of wind-solar hybrid power plants using the expected energy not supplied method. The location of this research is wind-solar hybrid power plants Pantai Baru, Bantul, Special Region of Yogyakarta, Indonesia. The method to determine the reliability of the power plant is the expected energy not supplied (EENS) method. This analysis used hybrid plant operational data in 2018. The results of the analysis have been done on the Pantai Baru hybrid power plant about reliability for electric power systems with EENS. The results of this study can be concluded that based on the load duration curve, loads have a load more than the operating kW of the system that is 99 kW. In contrast, the total power contained in the Pantai Baru hybrid power plant is 90 kW. This fact makes the system forced to release the load. The reliability index of the power system in the initial conditions, it produces an EENS value in 2018, resulting in a total value of 2,512% or 449 kW. The EENS value still does not meet the standards set by the National Electricity Market (NEM), which is <0.002% per year. Based on this data, it can be said that the reliability of the New Coast hybrid power generation system in 2018 is in the unreliable category.

An algorithm comparison for dynamic optimization problems

2012 ◽  
Vol 12 (10) ◽  
pp. 3176-3192 ◽  
Author(s):  
Ignacio G. del Amo ◽  
David A. Pelta ◽  
Juan R. González ◽  
Antonio D. Masegosa
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Algorithm Comparison
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