scholarly journals A Stochastic Optimization Method for Energy Storage Sizing Based on an Expected Value Model

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 702 ◽  
Author(s):  
Delong Zhang ◽  
Jianlin Li ◽  
Xueqin Liu ◽  
Jianbo Guo ◽  
Shaohua Xu
Keyword(s):  
Energy Storage ◽  
Stochastic Optimization ◽  
Storage System ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Expected Value ◽  
Total System ◽  
Deterministic Method ◽  
Expected Value Model ◽  
Value Model

Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method.

scholarly journals Stochastic Optimization Method for Energy Storage System Configuration Considering Self-Regulation of the State of Charge

2022 ◽  
Vol 14 (1) ◽  
pp. 553
Author(s):  
Delong Zhang ◽  
Yiyi Ma ◽  
Jinxin Liu ◽  
Siyu Jiang ◽  
Yongcong Chen ◽  
...  
Keyword(s):  
Energy Storage ◽  
Probability Distribution ◽  
Stochastic Optimization ◽  
Storage System ◽  
Self Regulation ◽  
Optimization Method ◽  
Energy Storage System ◽  
Distribution Model ◽  
The Impact ◽  
Pv Power Generation

Photovoltaic (PV) power generation has developed rapidly in recent years. Owing to its volatility and intermittency, PV power generation has an impact on the power quality and operation of the power system. To mitigate the impact caused by the PV generation, an energy storage (ES) system is applied to the PV plants. The capacity configuration and control strategy based on the stochastic optimization method have become an important research topic. However, the accuracy of the probability distribution model is insufficient and a stochastic optimization method is rarely used in a control strategy. In this paper, a stochastic optimization method for the energy storage system (ESS) configuration considering the self-regulation of the battery state of charge (SoC) is proposed. Firstly, to reduce the sampling error when typical scenarios of PV power are generated, a time-divided probability distribution model of the ultra-short-term predicted error of PV power is established. On this basis, to solve the problem that SoC reaches the threshold frequently, a self-regulation model of the SoC based on multiple scenarios is established, which can regulate the SoC according to rolling PV power prediction. A stochastic optimization configuration model of the energy storage system is constructed, which can reduce the impact of PV uncertainty on the configuration result. Finally, the proposed stochastic optimization method is validated. The fitting error of the time-divided probability distribution model is 15.61% lower than that of the t-distribution. The expected revenue of the optimal configuration in this paper is 8.86% higher than the scheme with a fixed probability distribution model, and 16.87% higher than without considering the stochastic optimization method.

scholarly journals A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1605 ◽  
Author(s):  
Hong-Chao Gao ◽  
Joon-Ho Choi ◽  
Sang-Yun Yun ◽  
Seon-Ju Ahn
Keyword(s):  
Scheduling Algorithm ◽  
Storage System ◽  
Operating Cost ◽  
Optimization Method ◽  
Energy Storage System ◽  
Power Sharing ◽  
Mixed Integer ◽  
Total System ◽  
Peak Demand ◽  
Sharing Scheme

As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.

scholarly journals Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 883 ◽  
Author(s):  
Jeseok Ryu ◽  
Jinho Kim
Keyword(s):  
Energy Storage ◽  
Cooperative Game ◽  
Demand Response ◽  
Storage System ◽  
Analytical Data ◽  
Energy Storage System ◽  
Gaussian Mixture ◽  
Energy Trading ◽  
Considerable Uncertainty

This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.

Stochastic Optimization of Nonlinear Energy Sinks Using Resonance-Based Clustering

2016 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum
Keyword(s):  
Stochastic Optimization ◽  
Adaptive Sampling ◽  
Dimensional Space ◽  
Optimization Method ◽  
Expected Value ◽  
Design Parameters ◽  
Support Vector ◽  
Design Variables ◽  
Energy Dissipated ◽  
Nonlinear Energy

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

The DNA Genetic Algorithm Applied for Solving Optimal Placement of Sensors

2012 ◽  
Vol 160 ◽  
pp. 103-108
Author(s):  
Xin Liu ◽  
Zhong Ren Feng ◽  
Xiong Jian Wang ◽  
Bao Fu Wang
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Expected Value ◽  
Optimal Placement ◽  
Stochastic Integer Programming ◽  
Objective Functions ◽  
Stochastic Variable ◽  
Expected Value Model ◽  
Complex Knowledge ◽  
Value Model

In order to solve optimal placement of bridge sensors based on the modal information, a multi-objective integer programming expected value model is established and the number of modal is considered as a stochastic variable in this paper, and here Fisher and MAC matrix are combined as the objective functions . Since DNA Genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly, these merits and the technique of stochastic simulation are also combined, which for estimating stochastic integer programming expected value models problem. And finally the feasibility of the algorithm is showed by XuGe Bridge as an example.

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