Local voltage control strategies for PV storage systems in distribution grids

Appen, Jan von; Stetz, Thomas A.; Braun, Martin; Schmiegel, Armin U.

doi:10.1109/pesgm.2014.6939234

Cited by 34 publications

(48 citation statements)

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“…Residential electric energy storage systems coupled with a photovoltaic (PV) installation could contribute to the stability of the low-voltage grid in the case of high PV penetration by absorbing the production power peaks around midday [1,2,3,4,5]. Moreover, such a system increases PV selfconsumption, which can provide an economic benefit to the system owner due to lower electricity exchange with the grid and minimized electricity transport losses [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Control algorithm for a residential photovoltaic system with storage

2017

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High penetration of photovoltaic (PV) electricity could affect the stability of the low-voltage grid due to over-voltage and transformer overloading at times of peak production. Residential battery storage can smooth out those peaks and hence contribute to grid stability. A feed-in limit allows for the easy setting of a maximum power injection cap and motivates PV owners to increase their self-consumption. A simple control strategy for a residential battery system coupled with a PV system that maximizes selfconsumption and minimizes curtailment losses due to a feed-in limit is presented. The algorithm used in this strategy does not require a forecast of insulation conditions. The performance of this algorithm is compared to a second algorithm-a control strategy based on linear optimization using a forecast. Assuming an exact forecast, this second algorithm is very close to the maximum self-consumption and minimum curtailment losses achievable and can be used to benchmark the simple strategy. The results show that the simple strategy performs as well as the second algorithm with exact forecasts and performs significantly better than the second algorithm using real forecasts. Moreover, it is shown that this result is valid for a large range of storage capacities and PV sizes. Furthermore, it is shown that with a time resolution of 15 minutes for the input data (the resolution of most PV production and load data) self-consumption is overestimated by about 3 % and curtailment losses are underestimated by the same amount. Load sensitivity simulations show that different load curve shapes do not fundamentally change the results. Finally, to assess the effect of load aggregation, the case where the strategy is applied separately to 44 households with storage is compared to the case where it is applied to a centralized storage system of the same size as the total storage of the 44 households. The reduction of the curtailment losses with the number of aggregated houses is showed.Keywords: Photovoltaic (PV), Home storage system, Battery management strategy, PV integration into the electrical grid Highlights• A control strategy for a battery system coupled with a PV system is presented.• With a feed-in limit this strategy does not need a PV production forecast.• This strategy performs as well as a strategy relying on an exact forecast.• A relatively small storage size allows peak injection reduction of 50 %.

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Section: Introductionmentioning

confidence: 99%

Control algorithm for a residential photovoltaic system with storage

2017

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“…Therefore, it is almost impossible for them to solve power flow equations. One solution to this problem is that all customers use fixed constraints on real power feed-in, such as the 70% rule German utilities apply as we described earlier in the paper [18]:…”

Section: Consensus-based Voltage Regulationmentioning

confidence: 99%

“…Under the revised German Renewable Energies Act of 2012, PV systems smaller than 30 kW p either have to permanently limit their real power injection to 70% of the installed PV capacity or be equipped with remote curtailment control. PV systems with installed capacity of 30 kW p or above must always be able to be curtailed remotely [18] (hereinafter, we call this the 70% rule).…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Management of PV-Storage Systems for Voltage Rise Mitigation

Ansari

Simões

2017

Technol Econ Smart Grids Sustain Energy

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This paper develops a distributed consensusbased energy management scheme (EMS) for multiple photovoltaics+energy storage systems (PV+ESS) connected to a smart distribution network. First, each customer individually determines the optimal size and initial scheduling of ESS to be installed in parallel with PV over a dayahead planning horizon. The objective of installing ESS is to reduce the electricity bill while minimizing ESS aging effect. Then, after the day-ahead planning, customers manage their system in a near real-time fashion to account for voltage conditions in order to avoid voltage-rise-associated curtailments. Customers along the feeder communicate with each other and exchange information about voltage deviation at their nodes in order to reach a consensus about average voltage situation in the distribution network. Based on voltage situation, EMS updates the lower bound on net power exchange with the grid for the hour ahead. Also, an iterative method is proposed to determine the lifetime of ESS and the economic benefits gained over the lifetime. Simulation results for IEEE 33-bus test system prove the effectiveness of the proposed EMS and its financial viability. Keywords

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“…However, the design and operation of such systems are challenging due to the absence of the main grid supply [2,3]. Also due to the fact PV cannot operate during the night, the use of an energy storage device such as a battery can significantly improve the reliability of small autonomous PV systems [4,5].…”

Section: Introductionmentioning

confidence: 99%

Optimal Control Based on Maximum Power Point Tracking (MPPT) of an Autonomous Hybrid Photovoltaic/Storage System in Micro Grid Applications

Atawi

Kassem

2017

Energies

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This paper investigates how to increase the efficiency of a photovoltaic/energy storage generation unit supplying dynamic loads by regulating and managing both the photovoltaic generator and the storage battery charge-discharge modes. The proposed photovoltaic/energy storage unit is proposed to supply an induction motor driven industrial pump with controlled speed and/or a DC motor driven water pump. An optimal proportional-integral-derivative control based on an Artificial Bee Colony Optimization algorithm is used to regulate the photovoltaic generator in case of normal operation or in case of maximum power point tracking (MPPT) and to also control the battery storage charge discharge modes. A vector control based on the proposed optimal control is used to regulate the induction motor rotor speed at its low reference values needed by the industrial pump. First, a total model of the entire system is obtained. The controller performance with the proposed system is studied with both a DC motor and/or induction motor loads. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions and with good performance. Also, it is noticed that operating the photovoltaic base on maximum power point tracking condition will give about 43% extra generation power than the normal operation case.

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Local voltage control strategies for PV storage systems in distribution grids

Cited by 34 publications

References 0 publications

Control algorithm for a residential photovoltaic system with storage

Control algorithm for a residential photovoltaic system with storage

Distributed Energy Management of PV-Storage Systems for Voltage Rise Mitigation

Optimal Control Based on Maximum Power Point Tracking (MPPT) of an Autonomous Hybrid Photovoltaic/Storage System in Micro Grid Applications

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