Dynamic programming in power system
Robust Adaptive Dynamic Programming for Large-Scale Systems
This brief presents a new approach to decentralized control design of complex systems with unknown parameters and dynamic uncertainties. A key strategy is to use the theory of robust adaptive dynamic programming and the policy iteration technique. An iterative control algorithm is given to devise a decentralized optimal controller that globally asymptotically
Stochastic dynamic programming approach to managing power system
Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation.
Dynamic Programming-Based ANFIS Energy Management System
Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a
Dynamic Programming Method
EE8702 POWER SYSTEM OPERATION AND CONTROL Dynamic Programming Method • In dynamic programming method, the unit commitment table is to be arrived at for the complete load cycle. • Forward dynamic programming is suitable if the start-up cost of a unit is a function of the time it has been off-line(i.e, fixed amount)
Simulink-based programs for power system dynamic analysis
This paper introduces Simulink-based programs developed for dynamic analysis of electrical power systems. The program can be used for research studies or as a teaching tool. With the program, time-domain simulation, modal analysis, participation factor analysis and visualization, frequency response analysis, and design of conventional and intelligent
Robust Adaptive Dynamic Programming With an Application to
This brief presents a novel framework of robust adaptive dynamic programming (robust-ADP) aimed at computing globally stabilizing and suboptimal control policies in the presence of dynamic uncertainties.
A Dynamic Programming based method for optimizing power system
A Dynamic Programming based method for optimizing power system restoration with high wind power penetration Abstract: Power system restoration is very significant for the operation reliability. Although a totally blackout in today''s power system rarely happens, the operators still have to make the restoration strategies in advance by using
Dynamic Programming
The dynamic programming makes use of the concept of suboptimization and the principle of optimality in solving this problem. The concept of suboptimization and the principle of optimality are explained through the example of an initial value problem. A linear programming problem can be formulated as a dynamic programming problem.
Direct Heuristic Dynamic Programming Method for Power
Direct Heuristic Dynamic Programming Method for Power S ystem Stability Enhancement* Miao Yu,Chao Lu and Yongjun Liu Abstract! In this paper a neural network-based approximate is applied to
Dynamic Programming
Dynamic programming can be used to solve the optimal energy management problem defined in Sect. 3.4.1.The sequence of controls (u_k) (decisions) represents the power split between the internal combustion engine and the rechargeable energy storage system at successive time steps. The cost corresponds to fuel consumption, energy consumption,
Dynamic Programming 11
Dynamic Programming 11 Dynamic programming is an optimization approach that transforms a complex problem into a sequence of simpler problems; its essential characteristic is the multistage nature of the optimization procedure. More so than the optimization techniques described previously, dynamic programming provides a general framework
Stochastic dual dynamic programming for optimal power flow
In classic power systems planning the only sources of uncertainty are conventional demand, inflow to hydroelectric plants, and power plant outages. The authors in Papavasiliou et al. (2018) solve a multi-stage stochastic OPF problems based on stochastic dual dynamic programming (SDDP) using DC relaxations.
Optimal Control Strategy for Parallel Plug-in Hybrid Electric
In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the
Dynamic Programming Method to Optimally Select Power
This paper presents a novel dynamic programming (DP) technique for the determination of optimal investment decisions to improve power distribution system reliability metrics. This model is designed to select the optimal small-scale investments to protect an electrical distribution system from disruptions. The objective is to minimize distribution system reliability metrics: System
Robust Adaptive Dynamic Programming for Large-Scale Systems
An iterative control algorithm is given to devise a decentralized optimal controller that globally asymptotically stabilizes the system in question and is demonstrated via the online learning control of multimachine power systems with governor controllers. This brief presents a new approach to decentralized control design of complex systems with unknown parameters and
RSM-Based Approximate Dynamic Programming for Stochastic
The stochastic energy management (SEM) of power systems is computationally intractable due to its randomness, nonconvexity, and nonlinearity. To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does
Applications of Approximate Dynamic Programming in Power Systems
<P>This chapter presents application examples in the field of power systems control using adaptive-critic designs. These adaptive-critic techniques were introduced in Chapters 3 and 4, but this chapter does contain an in-depth review of the basic ideas and how they apply to the specific problems examined in this chapter. The primary systems examined here are the three-phase
Robust Adaptive Dynamic Programming
This chapter introduces a new concept of robust adaptive dynamic programming (RADP), a natural extension of ADP to uncertain dynamic systems. It presents an online learning strategy for the design of robust adaptive suboptimal controllers that globally asymptotically stabilize the system.
Iterative Dynamic Programming—An Efficient Method for
The operation of electrical networks, microgrids, or heterogeneous battery systems, especially the dispatch of single units within the system, requires sophisticated power flow control strategies. If objectives such as efficiency are demanded for the operation of the energy system, typical control strategies lack the ability to verify the optimality of the operation.
Distributed multi-agent transmission system restoration using dynamic
Power system restoration is devised as a single objective problem to minimize the energy not supplied (ENS), which is solved by bus agents using dynamic programming. The uncertainty of the wind and photovoltaic sources is considered in the corresponding agents, which is dealed by the Monte Carlo method.
Real-time optimization of the integrated gas and power systems
The first test systems consist of the IEEE 39-bus power system and the 14-node natural gas system. In the power system, there are 10 generation sites, including two wind farms, a GFG, and 7 CFGs. G6 and G9 are replaced by two wind farms. G3 is replaced by a 300 MW GFG. The battery storage system is located on bus 17 in the power system.
Modified Dynamic Programming Algorithm and Its
applied dynamic programming to power system optimization operations. Najafi [5] proposed a new harmonic search algorithm for unit combination problem, which greatly improved the computational efficiency; Ren et al. [6] proposed a dynamic programming method based on the priority order method, which is suitable for
OPTIMIZATION OF POWER SYSTEM OPERATION
This chapter introduces several major techniques for solving the unit commitment (UC) problem, such as the priority method, dynamic programming, and the Lagrange relaxation method. Several new algorithms are then added to tackle UC problems.
DYNAMIC PROGRAMMING BASIC CONCEPTS AND
is based. After that, a large number of applications of dynamic programming will be discussed. 1. Deterministic dynamic programming 1.1 The standard problem of dynamic programming Dynamic programming deals with sequential decision processes, which are models of dynamic systems under the control of a decision maker. At each
Energy management strategy based on dynamic programming
This paper proposes an energy management strategy for a fuel cell (FC) hybrid power system based on dynamic programming and state machine strategy, which takes into account the durability of the FC and the hydrogen consumption of the system. The strategy first uses the principle of dynamic programming to solve the optimal power distribution between the
6 FAQs about [Dynamic programming in power system]
What is dynamic programming algorithm?
As a global optimization algorithm, dynamic programming algorithm has the characteristics of multi-stage decision-making and recursive calculation. It can solve the theoretical optimal solutions of fuel economy, charging cost in energy management strategies, and provide optimization references for other control strategies.
Can dynamic programming improve energy management?
Research has shown that energy management strategies based on dynamic programming can achieve a 20 % to 50 % improvement in fuel economy and a 10 % to 30 % savings in charging costs.
What is robust adaptive dynamic programming (robust-ADP)?
As an illustrative example, the computational algorithm is applied to the controller design of a two-machine power system. This brief presents a novel framework of robust adaptive dynamic programming (robust-ADP) aimed at computing globally stabilizing and suboptimal control policies in the presence of dynamic uncertainties.
Can dynamic programming algorithms improve energy management neighborhood optimization control?
An in-depth analysis was conducted on the improvement research of dynamic programming algorithms in terms of "dimensional disaster" and real-time performance, as well as their performance in energy management neighborhood optimization control.
What is robust adaptive dynamic programming (RadP)?
This chapter introduces a new concept of robust adaptive dynamic programming (RADP), a natural extension of ADP to uncertain dynamic systems. It presents an online learning strategy for the design of robust adaptive suboptimal controllers that globally asymptotically stabilize the system.
What is iterative dynamic programming (IDP)?
Introducing iterative dynamic programming (IDP) by reducing the search grid of the optimal trajectory can effectively reduce the exponentially increased amount of computation and huge storage requirements, and can obtain the same or similar optimization effect as DP . IDP was first proposed by Luss in 1990.
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