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Exploiting the Flexibility Value of Virtual Power Plants through Market Participation in Smart Energy Communities
Published in Ehsan Heydarian-Forushani, Hassan Haes Alhelou, Seifeddine Ben Elghali, Virtual Power Plant Solution for Future Smart Energy Communities, 2023
Georgios Skaltsis, Stylianos Zikos, Elpiniki Makri, Christos Timplalexis, Dimosthenis Ioannidis, Dimitrios Tzovaras
Many studies model the mathematical problem as a mixed-integer linear problem, through the fast execution time that it is provided. However, some authors formulate the VPP issue with nonlinear constraints, and due to nonlinearity, they apply several techniques trying to avoid the possibility of generating local optimal solutions. Regarding discrete optimization problems, a widely used algorithm is the branch-and-bound technique [16]. The branch-and-bound method recursively divides the search space into smaller spaces, called branches by using estimated bounds to limit the number of possible solutions. Nevertheless, the formed tree may become enormous; in that case, the available memory will be drained. Dynamic programming is another optimization method that breaks down a complex problem into a group of simpler sub-problems, solving them separately just once and then storing their solution. Therefore, the optimal solution will be a combination of sub-problems' outcomes.
Geometric, Linear, and Dynamic Programming and Other Methods for Optimization
Published in Yogesh Jaluria, Design and Optimization of Thermal Systems, 2019
Dynamic programming is a useful technique for a variety of engineering and management problems, such as those encountered in plant layouts, transportation networks, pipeline for oil and water distribution, and manufacturing systems. Chemical engineers have extensively used dynamic programming for the design, optimization, and control of chemical reactors and processes. However, the use of this optimization technique for thermal processes and systems is rather limited because it is often difficult to divide continuous processes into steps. When stages do arise, as in heating and cooling systems, the number of stages is often small and is generally not interchangeable or movable within the process.
Maintenance Logistics
Published in José Manuel Torres Farinha, Asset Maintenance Engineering Methodologies, 2018
Dynamic programming is a method that permits solving problems through solutions based on successively solving similar but smaller problems. This approach is used in algorithmic tasks in which the solution of a bigger problem is relatively easy to find if there are solutions for its subproblems. Dynamic programming is an algorithmic technique that is usually based on a recurrent formula and one or some of the starting states. A subsolution of the problem is constructed from previously found ones. Dynamic programming permits the transformation of a complex problem into a sequence of simpler problems. Its essential characteristic is the multistage nature of the optimization procedure.
Big data analytics energy-saving strategies for air compressors in the semiconductor industry – an empirical study
Published in International Journal of Production Research, 2022
Kuo-Hao Chang, Yi-Jyun Sun, Chi-An Lai, Li-Der Chen, Chih-Hung Wang, Chung-Jung Chen, Chih-Ming Lin
In this section, we describe how an optimisation method can be applied, in conjunction with the predictive models, to increase energy efficiency and thus cut production costs. One of the most powerful techniques used for solving optimisation problems is dynamic programming, an approach similar to divide and conquer algorithms. However, while the latter approach breaks a problem down into independent subproblems whose solutions are found recursively and then combined to solve the original problems, dynamic programming involves non-independent subproblems that are combined into ever-larger subproblems to solve the original problem (Réveillac 2015). Thus, dynamic programming can be highly efficient in that it solves each subproblem just once and stores the result in a table so that it can be repeatedly retrieved if needed. Dynamic programming has applications in manufacturing management and regulation, stock management, investment strategy, macro planning, training, game theory, computer theory, and systems control, among others (Réveillac 2015). In fact, the generalised optimisation algorithm used in this research is based primarily on dynamic programming, but as will be shown later, it is a smart dynamic programming algorithm in that it allows users to take into account the inherent trade-off between how fast the solution is achieved and how precisely the decision variables are optimised.
Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning
Published in Quality Engineering, 2022
Nooshin Yousefi, Stamatis Tsianikas, David W. Coit
Dynamic programming is an algorithm used to solve complex problems. The problem is solved in distinct stages using recursive functions. The solution of each stage or sub-problem is stored and reused to find the overall optimal solution of the problem. In this paper, dynamic programming is used to find the best policy of Markov decision processes using reinforcement learning. The Bellman equation in the Q learning algorithm, decomposes the overall optimal value into the optimal policy of each step and optimal value of remaining steps. The value function can be used to restore and retrieve the solution of each sub-problem. Q-learning is a well-known algorithm, as a method of dynamic programming, to solve the reinforcement learning problems, which is proposed by Watkins (Watkins 1989). In the Q-learning method, the agent takes one action at any particular state and evaluates its consequences, and by trying actions in all the possible states it learns what are the best actions which have the best long run rewards.
An ordering policy for deteriorating items with price-dependent iso-elastic demand under permissible delay in payments and price inflation
Published in Mathematical and Computer Modelling of Dynamical Systems, 2019
Puspita Mahata, Gour Chandra Mahata, Avik Mukherjee
This research uses the dynamic programming to solve the pricing and inventory problems. Dynamic programming is a method for solving complex problems by breaking them down into simpler sub-problems, which has also been applied to solve problems in economics and finance. Grüne and Semmler [72] used dynamic programming with adaptive grid scheme for optimal control problems in economics. Their study examples include economic growth, investment theory, environmental and resource economics. Grüne et al. [73] examined a company’s default risk in the context of a dynamic decision problem where companies can borrow from the credit market for investment. Then, Semmler and Bernard [74] employed an algorithm [36] created by Grüne and Semmler [72] to solve some dynamic optimizations. They studied the role of complex securities in the financial market meltdown and how they exacerbate leverage cycles. The major assumptions used in the above research articles are summarized in Table 1.