Pricing-based energy storage sharing and virtual capacity allocation

2017 ◽  
Author(s):  
Dongwei Zhao ◽  
Hao Wang ◽  
Jianwei Huang ◽  
Xiaojun Lin
Keyword(s):  
Energy Storage ◽  
Capacity Allocation

scholarly journals Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1642 ◽  
Author(s):  
Kan Xie ◽  
Weifeng Zhong ◽  
Weijun Li ◽  
Yinhao Zhu
Keyword(s):  
Energy Storage ◽  
Convex Optimization ◽  
Online Algorithm ◽  
Current System ◽  
Capacity Allocation ◽  
The United States ◽  
Distributed Implementation ◽  
Online Convex Optimization ◽  
System States ◽  
The One

This paper studies capacity allocation of an energy storage (ES) device which is shared by multiple homes in smart grid. Given a time-of-use (TOU) tariff, homes use the ES to shift loads from peak periods to off-peak periods, reducing electricity bills. In the proposed ES sharing model, the ES capacity has to be allocated to homes before the homes’ load data is completely known. To this end, an online ES capacity allocation algorithm is developed based on the online convex optimization framework. Under the online algorithm, the complex allocation problem can be solved round by round: at each round, the algorithm observes current system states and predicts a decision for the next round. The proposed algorithm is able to minimize homes’ costs by learning from home load data in a serial fashion. It is proven that the online algorithm can ensure zero average regret and long-term budget balance of homes. Further, a distributed implementation of the online algorithm is proposed based on alternating direction method of multipliers framework. In the distributed implementation, the one-round system problem is decomposed into multiple subproblems that can be solved by homes locally, so that an individual home does not need to send its private load data to any other. In simulation, actual home load data and a TOU tariff of the United States are used. Results show that the proposed online approach leads to the lowest home costs, compared to other benchmark approaches.

scholarly journals Typical Daily Power Curve Mining for Energy Storage Systems under Smoothing Power Fluctuation Scenarios

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiyun Yang ◽  
Jie Ren ◽  
Xiangjun Li ◽  
Hang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Energy Storage ◽  
Probability Distribution ◽  
Storage System ◽  
Capacity Allocation ◽  
Power Curve ◽  
Power Fluctuation ◽  
Quantum Genetic Algorithm ◽  
Weather Patterns ◽  
Sample Time

Under the application scenario of smoothing photovoltaic (PV) power fluctuation, a novel typical daily power curve mining method is developed for a battery energy storage system (BESS) that utilizes the power probability distribution and Bloch spherical quantum genetic algorithm. The charging/discharging of BESS is analyzed by applying fuzzy-c means clustering techniques. In the mining approach, at any sample time, those distribution intervals containing concentrated power points are individually located by using probability distribution information and Bloch spherical quantum genetic algorithm. Character power for the specified interval can also be determined using Bloch spherical quantum genetic algorithm. Next, a roulette principal is employed, to determine one value from the character power data as a typical value of the mined power curve at the sample time. By connecting the typical power at each sample time, the typical daily power curve for BESS is achieved. Based on typical power curve, decision-maker can master the important operating parameters of BESS and analyze optimal capacity allocation. By error evaluation indexes between the mined typical daily power curve and power curve under different weather patterns, the simulation results verify that the mined power curve can address the operating power of the BESS under different weather patterns.

scholarly journals Capacity allocation of HESS in micro-grid based on ABC algorithm

2020 ◽  
Vol 15 (4) ◽  
pp. 496-505 ◽  
Author(s):  
Yu Zhang ◽  
Zhe Yan ◽  
Cui Cui Zhou ◽  
Tie Zhou Wu ◽  
Yue Yang Wang
Keyword(s):  
Energy Storage ◽  
Fuzzy Control ◽  
Time Constant ◽  
Capacity Allocation ◽  
Pass Filter ◽  
Abc Algorithm ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Battery Capacity ◽  
Micro Grids

Abstract The hybrid energy storage system (HESS) is a key component for smoothing fluctuation of power in micro-grids. An appropriate configuration of energy storage capacity for micro-grids can effectively improve the system economy. A new method for HESS capacity allocation in micro-grids based on the artificial bee colony (ABC) algorithm is proposed. The method proposed a power allocation strategy based on low pass filter (LPF) and fuzzy control. The strategy coordinates battery and supercapacitor operation and improves the battery operation environment. The fuzzy control takes the state of charge (SOC) of the battery and supercapacitors as the input and the correction coefficient of the time constant of the LPF filter as the output. The filter time constant of the LPF is timely adjusted, and the SOC of the battery and supercapacitor is stable within the limited range so that the overcharge and over-discharge of the battery can be avoided, and the lifetime of the battery is increased. This method also exploits sub-algorithms for supercapacitors and battery capacity optimization. Besides, the Monte Carlo simulation of the statistic model is implemented to eliminate the influence of uncertain factors such as wind speed, light intensity and temperature. The ABC algorithm is used to optimize the capacity allocation of hybrid energy storage, which avoids the problem of low accuracy and being easy to fall into the local optimal solution of the supercapacitors and battery capacity allocation sub-algorithms, and the optimal allocation of the capacity of the HESS is determined. By using this method, the number of supercapacitors required for the HESS is unchanged, and the number of battery is reduced from 75 to 65, which proves the rationality and economy of the proposed method.

scholarly journals Optimal capacity allocation of multiple energy storage considering microgrid cost

2018 ◽  
Vol 1074 ◽  
pp. 012126
Author(s):  
Yuan Tian ◽  
Xiangyu Li ◽  
Yongqiang Zhu ◽  
Ruihua Xia
Keyword(s):  
Energy Storage ◽  
Capacity Allocation ◽  
Optimal Capacity

scholarly journals Optimal Capacity Allocation of Energy Storage System considering Uncertainty of Load and Wind Generation

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Leijiao Ge ◽  
Shuai Zhang ◽  
Xingzhen Bai ◽  
Jun Yan ◽  
Changli Shi ◽  
...  
Keyword(s):  
Energy Storage ◽  
Distribution System ◽  
Storage System ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Energy Storage System ◽  
Capacity Allocation ◽  
Allocation Model ◽  
Optimal Capacity ◽  
Node Distribution

Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses.

The capacity allocation method of photovoltaic and energy storage hybrid system considering the whole life cycle

2020 ◽  
Vol 275 ◽  
pp. 122902 ◽  
Author(s):  
Junhui Li ◽  
Zheshen Zhang ◽  
Baoxing Shen ◽  
Zhuo Gao ◽  
Dexuan Ma ◽  
...  
Keyword(s):  
Life Cycle ◽  
Energy Storage ◽  
Hybrid System ◽  
Capacity Allocation ◽  
Allocation Method ◽  
Whole Life Cycle
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