- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Novel Multi-objective Dynamic Programming Optimization Method: Performance Management of a Solar Thermal Power Plant as a Case Study
摘要： Due to the intermittent nature of solar irradiance, solar power plants are usually equipped with energy storage systems. Suitable charge and discharge management of the storage systems can considerably help increase the reliability and profitability of the solar systems. In this regard, various optimization approaches, with their own strengths and limitations, have been employed by literature. Dynamic Programming (DP) is one of the fittest approaches for the wide range of engineering problems which exhibit the properties of overlapping sub-problems. DP is a simple, gradient-free, efficient, and deterministic optimization method that guarantees the optimal solution. However, this method has not been developed for multi-objective problems. This study develops a multi-objective DP method and employs it for the performance management of a solar power plant equipped with thermal energy storage system. “Daily electricity generation” and “daily revenue obtained from selling electricity” are considered to be the objective functions. The superiority of the developed method is shown through a comparison with one of the most commonly used multi-objective optimization approaches, NSGA-II. This comparison indicates that the multi-objective DP attains 3.0%- 7.5% greater values of electricity generation and 3.1%-12.6% higher values of revenue than NSGA-II, for the different levels of solar radiation.
关键词： Thermal Energy Storage System,Optimal Performance,Solar Thermal Power Plant,Multi-objective Dynamic Programming method
更新于2025-09-23 15:21:21
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Design, Simulation and Fabrication of a High Gain Low Sidelobe Level Waveguide Slot Array Antenna at X-band with Zero Beam Tilts in Both Azimuth and Elevation Directions
摘要： This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid’s system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.
关键词： microgrid,Adaptive dynamic programming,reinforcement learning,evolutionary computing,dynamic energy management system (DEMS),renewable energy,neural networks
更新于2025-09-23 15:21:01
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Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid
摘要： In electrical systems, the main objective is to satisfy the load demand at the least cost without having imbalance between generation and consumption. Thus, the uncertainty of photovoltaic (PV) power production must be considered in generation planning of a power system. In this paper, we develop a modeling method of this uncertainty to consider it into the generation scheduling. The optimal generation scheduling in an urban microgrid is made by taking in consideration the operating reserve provision under stochastic characteristics of PV power prediction. By considering a prescribed risk level of unbalancing, a dynamic programming algorithm sets the operational planning of conventional generators by solving a non-convex mixed-integer nonlinear programming model, so that the operational cost and available operating reserve can be calculated. Then, the effect of PV power uncertainty into the unit commitment is analyzed by considering PV forecast intervals with a 95 % confidence level. The unit commitment is then recalculated with new generator set points and the same criteria. Finally, variations of the targeted minimized costs and obtained OR is analyzed according to the uncertainty.
关键词： Dynamic programming,Micro grid,Uncertainty,Unit commitment,Operating reserve
更新于2025-09-23 15:21:01
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[IEEE 2019 Computing, Communications and IoT Applications (ComComAp) - Shenzhen, China (2019.10.26-2019.10.28)] 2019 Computing, Communications and IoT Applications (ComComAp) - A Filtering Slot Antenna by the Air-Filled Annular Waveguide Structure
摘要： Receiver operating characteristic (ROC) curve is a plot traced out by pairs of false-positive rate and true-positive rate according to various decision threshold settings. The area under the ROC curve (AUC) is widely used as a figure of merit to summarize a diagnostic system’s performance, a binary classifier’s overall accuracy, or an energy detector’s power. Exploiting the equivalent relationship between the sample version of AUC and Mann Whitney U statistic (MWUS), in this paper, we develop an efficient algorithm of linearithmic order, based on dynamic programming, for unbiased estimation of the mean and variance of MWUS. Monte Carlo simulations verify our algorithmic findings.
关键词： Area under the curve (AUC),Mann-Whitney U statistic (MWUS),receiver operating characteristic (ROC),dynamic programming
更新于2025-09-19 17:13:59
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[Institution of Engineering and Technology 5th IET International Conference on Clean Energy and Technology (CEAT2018) - Kuala Lumpur, Malaysia (5-6 Sept. 2018)] 5th IET International Conference on Clean Energy and Technology (CEAT2018) - Conducting Polymers: New Arena in Dye-sensitized Solar Cells
摘要： The combined heat and power (CHP) systems can provide heat and electricity simultaneously. They are promising in the future energy landscape because of high efficiency and low emissions. This paper proposes a new operation optimization model of CHPs in deregulated energy markets. Both CHPs’ overall efficiency and heat to electricity ratio are closely linked with the loading level, which are dynamically determined in this paper. A discrete optimization model is then proposed to determine the optimal real-time operation strategies for the CHPs. The optimization problem is solved by the interior point method with discrete time intervals, in which the discrete optimal operation points can be identified effectively. This step projects the potential operation strategies that could produce maximum benefits. Finally, a dynamic programming algorithm is developed to maximize the profits of CHPs through dynamically modifying the operation strategies projected in the previous step considering transient constraints. The proposed new methodology is demonstrated on a 1-MW CHP system with real-time data.
关键词： heat to electricity (H2E) ratio,operation optimization model,Combined heat and power (CHP),maximum benefits,dynamic programming
更新于2025-09-19 17:13:59
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Coherent control of a GaAs quantum dot spin qubit operated in a feedback loop
摘要： The combined heat and power (CHP) systems can provide heat and electricity simultaneously. They are promising in the future energy landscape because of high efficiency and low emissions. This paper proposes a new operation optimization model of CHPs in deregulated energy markets. Both CHPs’ overall efficiency and heat to electricity ratio are closely linked with the loading level, which are dynamically determined in this paper. A discrete optimization model is then proposed to determine the optimal real-time operation strategies for the CHPs. The optimization problem is solved by the interior point method with discrete time intervals, in which the discrete optimal operation points can be identified effectively. This step projects the potential operation strategies that could produce maximum benefits. Finally, a dynamic programming algorithm is developed to maximize the profits of CHPs through dynamically modifying the operation strategies projected in the previous step considering transient constraints. The proposed new methodology is demonstrated on a 1-MW CHP system with real-time data.
关键词： heat to electricity (H2E) ratio,operation optimization model,Combined heat and power (CHP),maximum benefits,dynamic programming
更新于2025-09-19 17:13:59
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[IEEE 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) - Dubai, United Arab Emirates (2020.1.9-2020.1.10)] 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) - 35.83% Efficient Non-Toxic Perovskite Solar Cell using Copper Iodide and Tin-oxide
摘要： Receiver operating characteristic (ROC) curve is a plot traced out by pairs of false-positive rate and true-positive rate according to various decision threshold settings. The area under the ROC curve (AUC) is widely used as a figure of merit to summarize a diagnostic system’s performance, a binary classifier’s overall accuracy, or an energy detector’s power. Exploiting the equivalent relationship between the sample version of AUC and Mann Whitney U statistic (MWUS), in this paper, we develop an efficient algorithm of linearithmic order, based on dynamic programming, for unbiased estimation of the mean and variance of MWUS. Monte Carlo simulations verify our algorithmic findings.
关键词： receiver operating characteristic (ROC),dynamic programming,Area under the curve (AUC),Mann-Whitney U statistic (MWUS)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES) - Kremenchuk, Ukraine (2019.9.23-2019.9.25)] 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES) - Economic Efficiency of a Photovoltaic Power Plants
摘要： This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid’s system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.
关键词： microgrid,Adaptive dynamic programming,reinforcement learning,evolutionary computing,dynamic energy management system (DEMS),renewable energy,neural networks
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Optimal Allocation of Photovoltaic in the Hybrid Power System using Knapsack Dynamic Programming
摘要： Indonesia, which is represented by PLN as stated-owned electric utility company, commits to increase the share of renewable energy mix up to 23% in 2025. One way to reach this goal is the commitment to solar power generation. However, generation from photovoltaic has intermittency that dependent on sun irradiation, so the stability of power system might be affected in certain cases. The stability problem can be anticipated by using a hybrid power system with the composition of photovoltaic, battery, and diesel. Optimal power flow and N-1 contingency simulation is conducted to determine and compare locations and the capacity of hybrid power system. Furthermore the composition of hybrid power system is evaluated by an frequency stability simulation and the LCOE is calculated. The appropriate hybrid power system is selected by applying a knapsack dynamic programming approach. The result of the calculation is the optimal location of the hybrid power system with photovoltaic capacity as planned. This method is applied to Sumbawa power system as a case study.
关键词： power system stability,smart grids,cost,dynamic programming,hybrid power system
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE Milan PowerTech - Milan, Italy (2019.6.23-2019.6.27)] 2019 IEEE Milan PowerTech - Stochastic optimization framework for online scheduling of an EV charging station in a residential place with photovoltaics and energy storage system
摘要： House and building energy management systems (HEMS) are becoming key when it comes to assure grid stability and to offer flexibility. At the same time, energy systems technology has evolved to enable energy storage systems and electric vehicles to be managed together with local generated energy taking into consideration the preferences of the household owner. Contributing to this tendency, this work presents a stochastic optimization platform (SOFW) for optimal control using dynamic programming and stochastic optimization models. A stochastic optimization model involving a household composed of photovoltaics, energy storage system and an electric vehicle is designed and tested within SOFW. The uncertainties of the plug-in time and state of charge of the battery of the electric vehicle are modeled using a Markovian process and a Monte-Carlo simulation. The results showed that the proposed stochastic optimization model can be solved using dynamic programming and deployed as a continuous optimal control within SOFW. The system will be deployed shortly in Italy within one use case of the Storage4Grid (S4G) project.
关键词： stochastic dynamic programming,optimization framework,energy storage systems,Markov processes,electric vehicle charging
更新于2025-09-11 14:15:04