Stochastic Assessment of Voltage Unbalance Due to Single-Phase-Connected Solar Power

Schwanz, Daphne; Möller, Friedemann; Rönnberg, Sarah; Meyer, Jan; Bollen, Math

doi:10.1109/tpwrd.2016.2579680

Cited by 85 publications

(35 citation statements)

References 22 publications

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“…In literature, a comprehensive analysis was conducted in [12] where several influence factors were considered such as background voltage unbalance, solar inverter penetration level and inverter size affecting the voltage unbalance. The results of stochastic voltage unbalance assessment were compared with the deterministic voltage unbalance calculation and the results of stochastic assessment demonstrated bigger flexibility in results analysis.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo-Based Comprehensive Assessment of PV Hosting Capacity and Energy Storage Impact in Realistic Finnish Low-Voltage Networks

2018

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The direction taken towards sustainable power system and renewable energy generation is now irreversible. The power grid needs to host more renewable energy sources, such as solar power, and tackle power quality problems that come along with it. In this paper, firstly, the Hosting Capacities (HCs), of Photo-Voltaic (PV), were found for various regions and their limiting constraints were defined. Afterwards, comparison was made with the HC values obtained for different voltage value standards defined by various countries. Next, single-phase PV connection percentages in the network were defined that makes the voltage unbalance the limiting factor for HC. Lastly, the HC of the solar generation coupled with a Battery Energy Storage System (BESS) was assessed for the Finnish Low-Voltage (LV) grids. Different BESS-based scenarios were employed and their impact on voltage unbalance and HCs were observed. Finally, also the load voltage unbalance was incorporated to make the approach realistic and its impact on HC was analyzed. Results reveal that, depending on the connection strategy, the BESS can increase as well as decrease the HC based on voltage unbalance criteria. However, the load voltage unbalance has little effect on the solar HC values.

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Section: Introductionmentioning

confidence: 99%

Monte Carlo-Based Comprehensive Assessment of PV Hosting Capacity and Energy Storage Impact in Realistic Finnish Low-Voltage Networks

2018

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“…Studies towards estimating hosting capacity are common part of studies by large network operators as part of their strategies towards allowing the integration of more renewable electricity production into their electricity networks [31][32][33]. An important recent development is the introduction of a stochastic approach to hosting capacity [34][35][36][37][38][39]. The stochastic element introduced concerns especially the unknown location of future PV installations in the distribution grid, but it can be extended to include other uncertainties (see Section 2.2).…”

Section: Definition and Aim Of Hosting Capacitymentioning

confidence: 99%

“…To illustrate the calculation of the hosting capacity in a stochastic way for planning purposes, two low-voltage networks in the North of Sweden have been used: a 6-customer rural network; and a 28-customer suburban network. Both networks were used in [39], where the details of the networks can be found. Both networks contain only domestic customers and both networks are three-phase down to the customer.…”

Section: Two Swedish Low-voltage Networkmentioning

confidence: 99%

Hosting Capacity of the Power Grid for Renewable Electricity Production and New Large Consumption Equipment

2017

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After a brief historical introduction to the hosting-capacity approach, the hosting capacity is presented in this paper as a tool for distribution-system planning under uncertainty. This tool is illustrated by evaluating the readiness of two low-voltage networks for increasing amounts of customers with PV panels or with EV chargers. Both undervoltage and overvoltage are considered in the studies presented here. Probability distribution functions are calculated for the worst-case overvoltage and undervoltage as a function of the number of customers with PV or EV chargers. These distributions are used to obtain 90th percentile values that act as a performance index. This index is compared with an overvoltage or undervoltage limit to get the hosting capacity. General aspects of the hosting-capacity calculations (performance indices, limits, and calculation methods) are discussed for a number of other phenomena: overcurrent; fast voltage magnitude variations; voltage unbalance; harmonics and supraharmonics. The need for gathering data and further development of models for existing demand is emphasised in the discussion and conclusions.An international working group [22] recently presented a number of recommendations, key findings and open issues to be addressed because of the integration of solar power. Recommendations were given for a number of power-quality phenomena, including harmonics, supraharmonics, fast voltage variations (faster than 10-min time scale), slow voltage variations (slower than 10 min), overvoltage, flicker, and voltage unbalance. The hosting capacity approach, as was proposed by an international cooperation project in 2004 (see Section 2) is put forward in the report as an important tool for quantifying the impact of solar power on the power system. Additional work is needed, according to the report, for establishing suitable performance indices and corresponding limits.This paper consists of three main parts. It starts in Section 2 with an overview, partly from a historical insider perspective, of the hosting-capacity approach. This part also includes a discussion on different kinds of uncertainly. A hosting-capacity-based planning approach is presented in Section 3, applied in Section 4.1 through Section 4.6 and discussed in Section 4.7. The description and the application are for overvoltage and undervoltage as these are the phenomena, with the possible exception of overcurrent, for which the best calculation tools, performance indices and limits are available. The third part of the paper contains a detailed discussion about including other phenomena in hosting-capacity studies. For each phenomenon, performance indices, limits and calculation methods are discussed. Hosting CapacityThe term "hosting capacity" was coined in the context of distributed generation by André Even in March 2004 during discussions within the integrated European EU-DEEP project [23,24]. An approach for quantifying the hosting capacity was developed further by others within that same project [25]. The term "hosting capa...

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“…i) For the definite-max scenario, the degree of power imbalance suggests the move of loads totaling 3 from the definite-max phase to the other two phases, where is given by (21). ii) For the definite-order scenario, the degree of power imbalance suggests the move of loads totaling 3 away from the definite-max phase and the move of 3 to the definite-min phase, where and are defined in (20).…”

Section: Degree Of Power Imbalancementioning

confidence: 99%

“…Reference [20] quantified current imbalance on short transmission lines. Reference [21] forecasted voltage imbalance on low voltage feeders with photovoltaic (PV) generation. Reference [22] converts three-phase imbalanced currents into two orthogonal AC currents with equal amplitudes.…”

Section: Introductionmentioning

confidence: 99%

Three Phase Power Imbalance Decomposition into Systematic Imbalance and Random Imbalance

Kong

2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Uneven load allocations and random load behaviors are two major causes for three-phase power imbalance. The former mainly cause systematic imbalance, which can be addressed by low-cost phase swapping; the latter contribute to random imbalance, which requires relatively costly demand-side managements. To reveal the maximum potential of phase swapping and the minimum need for demand-side managements, this paper first proposes a novel a priori judgment to classify any set of three-phase power series into one of four scenarios, depending on whether there is a definite maximum phase, a definite minimum phase, or both. Then, this paper proposes a new method to decompose three-phase power series into a systematic imbalance component and a random imbalance component as the closed-form solutions of quadratic optimization models that minimize random imbalance. A degree of power imbalance is calculated based on the systematic imbalance component to guide phase swapping. Case studies demonstrate that 72.8% of 782 low voltage substations have systematic imbalance components. The degree of power imbalance results reveal the maximum need for phase swapping and the random imbalance components reveal the minimum need for demand side management, if the three phases are to be fully rebalanced. Index Terms-low voltage distribution network, power imbalance, random imbalance, systematic imbalance, three phase electric power I. NOMENCLATURE The degree of power imbalance at time point The total number of time points ∅ where ∅ ∈ , , Phase ∅ power at time point ∅ where ∅ ∈ , , The average power of phase ∅ over time

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Stochastic Assessment of Voltage Unbalance Due to Single-Phase-Connected Solar Power

Cited by 85 publications

References 22 publications

Monte Carlo-Based Comprehensive Assessment of PV Hosting Capacity and Energy Storage Impact in Realistic Finnish Low-Voltage Networks

Monte Carlo-Based Comprehensive Assessment of PV Hosting Capacity and Energy Storage Impact in Realistic Finnish Low-Voltage Networks

Hosting Capacity of the Power Grid for Renewable Electricity Production and New Large Consumption Equipment

Three Phase Power Imbalance Decomposition into Systematic Imbalance and Random Imbalance

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