A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network

Rowe, Matthew; Yunusov, Timur; Haben, Stephen; Singleton, Colin; Holderbaum, William; Potter, Ben

doi:10.1109/tsg.2014.2323115

Cited by 61 publications

(53 citation statements)

References 26 publications

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“…This work addresses the on-line peak reduction storage control problem as described in [10,11,39] and discussed in the previous section. This section will define the storage system and introduce the characteristics of the demand profiles found on the LV network.…”

Section: The Energy Storage System and LV Network Demand Modelmentioning

confidence: 99%

“…The Results section, Section 6, presents a technique for finding the horizon time for peak reduction storage control. It has been shown that planning ahead is vital when controlling storage [11,12,39], and to highlight this point on the single phase of the LV network, the performance of the MPC controller is compared to a standard set-point controller in Section 6, where the set-point is found from a priori demand data. Current smart grid literature has begun to explore the benefits of stochastic control and, therefore, treating the demand as a stochastic element.…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

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The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid's infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, where the objective is to achieve the greatest peak reduction in demand, for a given storage device specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-point controller and bench marked against a control algorithm with a perfect forecast. A specific case study, using storage on the LV network, is presented, and the results of each algorithm are compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

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Section: The Energy Storage System and LV Network Demand Modelmentioning

confidence: 99%

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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“…4. It is assumed that BESS solutions, or more specifically battery energy storage solutions, start the simulations at 50% SOC and are not 100% efficient at storing and releasing electrical energy, as in [36]. Additionally, its utilisation will degrade the energy storage capability and performance over time, as shown in [37].…”

Section: Assumptionsmentioning

confidence: 99%

“…For this work, a well-established model that has been used in previous publications by this research group was used [36,40,41]. This model consists of a battery with a self-discharge loss that is dependent on the current battery's State Of Charge (SOC) and an energy conversion loss to represent the energy lost when charging or discharging this battery.…”

Section: Battery Modellingmentioning

confidence: 99%

Distributed Energy Storage Control for Dynamic Load Impact Mitigation

et al. 2016

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Abstract:The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network's voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation.

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“…), while fast loads characterised by short and high power demand are paired with flywheels, supercapacitors or other technologies capable of reacting in a very short time, outputting relatively high power [1][2][3]. Usually, the variability of the load also depends on the time constant of the application: the power demand in regional power network fluctuations is periodical with peaks occurring at around the same time of the day and of the year, leading to optimal solutions for power management that account for the deviation from the typical daily or yearly profile [4][5][6][7][8]. When loads are limited to a short period of time (tens of seconds or less), the variability tends to be defined by three main factors: when the demand occurs, its intensity and duration.…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

2016

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Abstract:In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs.

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A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network

Cited by 61 publications

References 26 publications

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

Distributed Energy Storage Control for Dynamic Load Impact Mitigation

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

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