# Review on Distribution Network Optimization under Uncertainty

## Abstract

**:**

## 1. Introduction

- (1)
- Planning: With the global trend of using more renewable energy to reduce emission, one of the challenges in distribution system planning nowadays is to integrate more distributed energy resources in existing networks by finding the optimal sizes of distributed generators (DG) and their installation locations (Section 3.2) while ensuring stable network operation [1]. With the increased load demand, aging facilities and limited network capacity, power quality (PQ) phenomena and constraint violation cause great financial loss to both Transmission System Operators (TSOs) and customers, thus proper and optimal installation of PQ mitigation devices is needed in order to provide sufficient power quality to customers (Section 3.3). Distribution system planning also looks into optimal meter placement [2] for the improvement of accuracy in state estimation (Section 3.1), and optimal strategy of network expansion/reinforcement in order to increase network capacity and facilitate network changes [3], among other factors.
- (2)
- Operation: This involves the daily management and operation in utilization of network analysis and optimization [4]. Operation becomes more challenging than ever because of the high penetration of renewable resources in networks, e.g., photovoltaic (PV) generation and wind turbines) [5]. The renewable energy sources in nature are highly stochastic and intermittent depending on the weather conditions. Without proper operation strategies, these renewable resources can cause instability and power quality issues in networks, such as unbalance phenomena and violation of thermal limits of the grid with high ramping in voltages and currents. Proper constraint management is required to ensure the network states within an acceptable range (Section 4.1). With the new increased flexibility and controllability, the resources in the network (including DG and load flexibility) can be utilized to achieve certain purposes, such as constraint management and solving congestion issues (Section 4.2).

## 2. Measurement and Uncertainty

#### 2.1. Measurements

#### 2.2. Uncertainty of Measurements

- (1)
- Real measurements: Usually the uncertainty of this type of measurements is determined by the tolerance of measurement devices. Usually the requirement of measurement accuracy complies with certain standards, and was specified already during the meter development/design stage based on the purpose of the applications. Thus, the measurement tolerance can be obtained by specific standards [24]. For instance, [25,26] specify the ranges of different classes of voltage transformers and current transformers, as well as their phase displacement. IEC61000-4-30 specifies the maximum allowed uncertainty of voltage and current measurements for classes A and B performance [27]. In [28], the range of voltage measurement tolerance was set based on both measurement and transformer uncertainty, and the tolerance of power measurement was set by considering both Current Transformers (CTs) and Voltage Transformers (VTs) tolerance [28].
- (2)
- Pseudo-measurement: As for PMs, its accuracy mainly depends on the performance of the estimator/forecaster and the credibility of the information used for estimation. PMs have a larger uncertainty/tolerance than real measurements. Therefore they are usually given lower influence on decision-making. In [29,30], 20% to 50% of errors were considered in PMs. In [18,31], the authors specify the PM errors for load demand under different scenarios. Generally, the PM of real power is smaller than that of reactive power, as more data sources (such as energy bill and scheduling) are available for the estimation of real power. The estimation (or indirect measurements) of network parameters also have certain levels of uncertainty. In [2], a range of tolerance for line impedances is specified. In [32,33], the authors provide the uncertainty of short-circuit impedances of general transformers and On-Load Tap-Changer Transformer (OLTCT) respectively. Table 1 summarizes the tolerance of a list of critical variables used in power system simulation [34].

## 3. Optimization-Based Distribution Planning

_{u}f(u,x),

s.t. g(u,x) = 0; h(u,x) ≤ 0 (including u

_{min}≤ u ≤ u

_{max}).

#### 3.1. Optimal Meter Placement

#### 3.2. Distribution Generations (DGs) Planning

#### 3.3. Power Quality Mitigation

_{TH}), the objective function in optimization can be defined as [78]:

#### 3.4. Addressing Uncertainty in Optimization Process

- (1)
- Uncertainties in operating conditions: The operation scenario varies throughout the whole year because of factors such as different DG outputs and the loading of different types of customers. This can be addressed by using historical data to generate the simulation conditions that approach the actual operating conditions. The electricity consumption patterns of different types of loads can be obtained from a survey [13]. The generation profiles of renewable energy, such as PV and wind turbines, can be estimated based on weather, or obtained from realistic output [83]. In [84], actual varying loading, PV and wind profiles in different counties in Europe for the past decade are provided. It provides a wide range of data for power system modelling and uncertainty analysis. Regarding PQ simulation, there is uncertainty of factors such as the fault rate and harmonic injection. These should be also considered when assessing the PQ performance [78].
- (2)
- Uncertainties in network topologies: Network topology should be provided in certain network analysis (such as with load flow and SE). The uncertainties of the frequent topology changes exit in distribution systems because of the operation of switching, which is adopted by grid operators to optimize the electricity provision even with the occurrence of outages. Without proper network topology, the analysis results are not accountable. The uncertainties of network behaviors and topologies on SE accuracy have been analyzed in [18,85], respectively. In [86], the uncertainty of network configuration was reduced using a recursive Bayesian approach together with utilizing the SE outputs.
- (3)
- Uncertainties in network parameters: Network parameters are usually not given directly, and their values can be estimated via indirect measurements or estimation. Thus, uncertainties exit in these estimated network parameters, such as line impedances [2], short-circuit impedances for transformers [32] and OLTCT impedance [33]. In [87], a method based on the artificial neural network (ANN) and topology observability is used to evaluate the parameters which are missing in power systems.

## 4. Optimization-Based Distribution Operation and Management

#### 4.1. Constraint Management

#### 4.2. Demand Side Management and Flexibility Exchange

## 5. Future Distribution Networks

#### 5.1. Big Data and Challenge

#### 5.2. Integrated Distribution Optimization between DSO and TSO

#### 5.3. Differentiated PQ Supply

## 6. Conclusions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Illustration of power quality (PQ) optimization/mitigation [76].

**Table 1.**Tolerance ranges for measurements and network variables [34]. PMs = pseudo-measurements.

Index | Variable | Tolerance Range |
---|---|---|

1 | Voltage measurement | [0.14%, 3.04%] with 3-sigma |

2 | Power measurements | [0.17%, 6.16%] with 3-sigma |

3 | PMs of active power | [10%, 40%] with 3-sigma |

4 | PMs of reactive power | [20%, 50%] with 3-sigma |

5 | Line impedance | [0, 20%] with 3-sigma |

**Table 2.**Categories of various optimization algorithms [42].

Rank | Categories | Algorithms |
---|---|---|

1 | AI techniques | genetic algorithm (GA), particle swarm optimization (PSO), tabu search (TS), fuzzy logic (FL), ant colony search (ACS), artificial bee colony (ABC), artificial neural network (ANN), simulated annealing (SA) |

2 | Conventional technique | residues, modal index techniques, eigen values, eigen vector, |

3 | Optimization techniques | dynamic programming, linear programming (LP), non-linear programming (NLP), interior point method, ordinal optimization (OO), gradient search method |

4 | Hybrid AI techniques | GA + FL, GA + optimal power flow (OPF), GA + PSO |

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**MDPI and ACS Style**

Liao, H.
Review on Distribution Network Optimization under Uncertainty. *Energies* **2019**, *12*, 3369.
https://doi.org/10.3390/en12173369

**AMA Style**

Liao H.
Review on Distribution Network Optimization under Uncertainty. *Energies*. 2019; 12(17):3369.
https://doi.org/10.3390/en12173369

**Chicago/Turabian Style**

Liao, Huilian.
2019. "Review on Distribution Network Optimization under Uncertainty" *Energies* 12, no. 17: 3369.
https://doi.org/10.3390/en12173369