Residential Distribution Network Research Articles

Aggregation of component-based grid-feeding DER and load models for simulation of microgrid islanding transients

The reliability of distribution networks can be improved by islanding of part of the network when a short-circuit fault occurs. The voltage and frequency stability during transient can be evaluated with time-domain simulations using component-based DER and load models. However, simulations with this type of models have a relatively high computational burden which constrains periodical stability analysis of microgrids. This paper proposes an aggregation methodology which significantly decreases the computation time of fault-initiated islanding simulations with component-based models, while maintaining the nonlinear dynamics. The methodology is first validated by directly comparing the original and equivalent models during large voltage and frequency transients. Secondly, a fault-initiated islanding case study of a residential distribution network is performed. For both validations, a sensitivity analysis of model parameters and voltage variations is performed. The results indicate the methodology preserves the dynamics of DERs and loads, while significantly reducing the computational time.

A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks

Demand Side Management (DSM) is becoming necessary in residential electricity distribution networks where local electricity trading is implemented. Amongst the DSM tools, Demand Response (DR) is used to engage the consumers in the market by voluntary disconnection of high consumption receptors at peak demand hours. As a part of the transition to Smart Grids, there is a high interest in DR applications for residential consumers connected in intelligent grids which allow remote controlling of receptors by electricity distribution system operators and Home Energy Management Systems (HEMS) at consumer homes. This paper proposes a novel algorithm for multi-objective DR optimization in low voltage distribution networks with unbalanced loads, that takes into account individual consumer comfort settings and several technical objectives for the network operator. Phase load balancing, two approaches for minimum comfort disturbance of consumers and two alternatives for network loss reduction are proposed as objectives for DR. An original and faster method of replacing load flow calculations in the evaluation of the feasible solutions is proposed. A case study demonstrates the capabilities of the algorithm.

An auxiliary unit with selective harmonic suppression and inherent reactive power compensation for civil distribution networks

In view of the fact that there are usually two dominant harmonic currents and large amounts of inductive loads in residential distribution networks and low voltage distribution networks (called civil distribution networks in the paper), an auxiliary unit with selective harmonic suppression and inherent reactive power compensation for civil distribution networks is proposed in this paper. The proposed auxiliary unit consists of a passive part and an active inverter part. The passive part is composed of a compensation capacitor and two series resonance circuits tuned at the frequencies of the two dominant current harmonics. The passive part is used to compensate reactive power with a constant capacity and reduces capacity requirements of the active inverter part as well as voltage ratings of active inverter part. Owing to the low-impedance of the series resonance circuits, the vast majority of compensation currents generated by the active inverter part are injected to grid. These characteristics have been verified by experiment results. It is indicated that the proposed auxiliary unit is a preferred topology for improving power quality in civil distribution networks.

Data-driven uncertainty analysis of distribution networks including photovoltaic generation

This paper investigates residential distribution networks with uncertain loads and photovoltaic distributed generation. An original probabilistic modeling of consumer demand and photovoltaic generation is presented that is based on the analysis of large set of data measurements. It is shown how photovoltaic generation is described by complex non-standard distributions that can be described only numerically. Probabilistic analysis is performed using an enhanced version of the Polynomial Chaos technique that exploits a proper set of polynomial basis functions. It is described how such functions can be generated from the numerically available data. Compared to other approximate methods for probabilistic analysis, the novel technique has the advantages of modeling accurately truly nonlinear problems and of directly providing the detailed Probability Density Function of relevant observable quantities affecting the quality of service. Compared to standard Monte Carlo method, the proposed technique introduces a simulation speedup that depends on the number of random parameters. Numerical applications to radial and weakly meshed networks are presented where the method is employed to explore overvoltage, unbalance factor and power loss, as a function of photovoltaic penetration and/or network configuration.

Fault Detection for Low-Voltage Residential Distribution Systems With Low-Frequency Measured Data

Increasing the number of active consumers in distribution networks necessitates transforming the current control, monitoring, and protection schemes. However, on one hand, installing high-frequency measurement devices and fast communication platforms in low-voltage (LV) distribution networks is not cost effective and scalable. On the other hand, the fault detection approaches, which can provide acceptable accuracy by relying only on low-frequency measured data (with 1–30-min sampling rates), are not developed yet. Currently, the overcurrent fault detectors work mainly based on fixed current thresholds, which makes them inefficient in a system with high-distributed-energy resources. This is due to high volatility and uncertainty in the measured profile of the current. In this article, a data-driven fault detection framework with dynamic fault current thresholds is proposed. The motivation here is to develop a framework that can locally detect and isolate faults within the LV distribution networks without requiring high-frequency sampling meters. The proposed model is based on quantile regression as a statistical method to generate the quantiles of distributions of the current measurements. Two different fault current thresholds are formulated for instantaneous and definite time fault detection schemes. The thresholds are dynamically predicted for each next time step. The proposed framework is evaluated using data from a real distribution network with 169 houses. The results suggest that the proposed model is very promising for LV residential distribution networks.

Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

Capacity challenges are becoming more frequent phenomena in residential distribution networks with new forms of loads, distributed renewable energy resources (RES) and price-responsive applications. Different types of demand response programs have been introduced to tackle these challenges through iterative changes in price and/or contractual participations based on incentives. In this research, a dynamic network tariff-based demand response program is proposed to address congestion problems in low-voltage (LV) networks. The formulation takes advantage of the scalable architecture of the agent-based systems that allows local decision making with limited communication. Energy consumption schedules are developed on a day-ahead basis depending on the expected cost of overloading for a number of probable scenarios. The performance of the proposed approach has been tested through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 82% reduction in congestion on a monthly basis, while maintaining the quality of supply in the network.

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks

Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs). Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.

Community electric vehicle load forecasting based on time series distribution

Electric vehicle charging load forecasting helps power system planning management and optimization. This paper proposes a residential private electric vehicle load forecasting model based on positive-reverse valley time charging, which predicts the load while satisfying the orderly charging of large-scale private electric vehicles in residential areas. Firstly, it analyzes the historical travel rules of private electric vehicles in the community and the charging and discharging characteristics of electric vehicles. Secondly, based on the peak-to- valley time-of-use(TOU) electricity price, the owner of the vehicle is fully utilized to make the electric vehicle orderly charging, so as to obtain the total charging load of the electric vehicle in the community; Finally, the actual data is used to predict the change of the load curve of private electric vehicles in the future after large-scale access to the grid. It is found that under the scenario of disorderly charging of private electric vehicles, the larger volume of the grid has the greater difference between the peaks and valleys of the grid. Through further calculations, it is found that the use of valley time charging can alleviate the impact of the electric vehicle charging load on the power grid to a certain extent. That also can effectively reduce the peak-to-valley difference of the grid load and residential area distribution network overload rate.

Day-Ahead Optimal Operation for Multi-Energy Residential Systems With Renewables

The intermittency and stochasticity nature of distributed renewable energy sources has introduced great challenges to the efficiency and security of energy distribution system operations. To address the negative impacts of intermittent renewable energy sources, this paper proposes a day-ahead optimal operation strategy utilizing distributed energy resources based on the framework of the interconnected multi-energy system. First, a framework and mathematical models of multi-energy residential systems (MERS) are proposed. Based on the characteristics of residential energy distribution networks, the complex MERS models are reformulated to relieve the computational burden. Furthermore, the uncertainty factors such as renewable energy generation fluctuations and demand variations are handled by a reformulated chance constrained programing technique. The feasibility and effectiveness of the proposed method are validated through a combined electric power and natural gas test system. Compared to similar models and methods in the existing literature, the proposed method performs better in terms of solution time, model scalability, and robustness in handling uncertainties.

Impact of low-carbon technologies on the power distribution network

Over recent decades, there has been a growing concern about how modern technologies would impact on an electrical grid. Whilst the UK is set to meet the target of 15% energy demand from renewable sources by 2020 to ensure energy security and decarbonisation objectives, there has been a greater concern over how the increasing deployment of low carbon technologies (LCTs) would affect an electrical grid, in particular the residential low voltage distribution network. While LCTs can provide clean energy and decreases the dependence on oil and natural gas stocks, their impact on distribution networks is unknown and the operators are visibly blind as they use a ‘fit and forget’ approach. Consumers’ connections are uncertain and stochastic and the LCT uptake poses a potential threat for distribution network operators. A review is presented in this paper, illustrating the potential impacts the LCTs have on a local electrical grid such as voltage regulation, thermal limits, power quality and harmonics. Potential solutions, such as Deregulation, Soft Open Points, On Load Tap Changer, and Active Power Filter currently available for Distribution Network Operators and power system planners before costly network reinforcement work is undertaken.

Risk Evaluation of Distribution Networks Considering Residential Load Forecasting with Stochastic Modeling of Electric Vehicles

Large‐scale integration of electric vehicles (EVs) into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Similarly, electric load forecasting is an operational process permitting the utilities to manage demand issues for optimal energy utilization. Unbalanced voltages prevent the effective and reliable operation of RDNs. This study implements a novel framework to examine risks associated with RDNs by applying a residential forecasting model with a stochastic model of EVs charging pattern. Diversified EV loads require a stochastic approach to predict EVs charging demand; consequently, a probabilistic model is developed to account for several realistic aspects comprising charging time, battery capacity, driving mileage, state‐of‐charge, travelling frequency, charging power, and time‐of‐use mechanism under peak and off‐peak charging strategies. Peak‐day forecast of various households is obtained in summer and winter by implementing an optimum nonlinear auto‐regressive neural‐network (NN) with time‐varying external input vectors (NARX). Outputs of the EV stochastic model and residential forecasting model obtained from Monte‐Carlo simulations and the NARX‐NN model, respectively, are utilized to evaluate power quality parameters of RDNs. Performance specifications of RDNs including voltage unbalance factor (VUF) and voltage behavior are assessed in context to EV charging scenarios with various charging power levels under different penetration levels.

Risk-Averse Optimal Bidding of Electric Vehicles and Energy Storage Aggregator in Day-Ahead Frequency Regulation Market

The need for frequency regulation capacity increases as the fraction of renewable energy sources grows in the electricity market. An aggregator can provide frequency regulation by controlling its generation and demand. Here we investigate the participation of an aggregator controlling a fleet of electric vehicles (EVs) and an energy storage (ES) in day-ahead regulation and energy markets and determine the optimal size of the aggregator's bids. The problem is formulated as a stochastic mixed integer linear programming model, taking into account the uncertainties regarding energy and frequency regulation prices. The risks associated with the uncertainties are managed using the conditional value-at-risk method. Because most EVs are charged in residential distribution networks, load flow constraints are also taken into account. A linear formulation based on the rainflow cycle counting algorithm is proposed to include the ES degradation costs incurred from following the frequency regulation signals into the objective function. The problem is studied within the context of a medium-voltage distribution network adopting the market rules of the California Independent System Operator. The results of the numerical analysis show how joint optimization of EVs and ES can improve the aggregator's profit, and verify that the proposed degradation cost formulation can effectively minimize the degradation costs of the ES.

Modelling of ampacity and temperature of MV cables in presence of harmonic currents due to EVs charging in electrical distribution networks

To meet the rapidly growing electric vehicle (EV) charging demand, it is important to determine maximum ampacity and avoid overloading of Medium Voltage (MV) cables in power distribution systems. Previous works on modelling and analysis of relationship between the load demand of MV cables and EV penetration level did not consider the contribution of harmonic currents. This paper presents a methodology for modelling and analysis of the impact of EV charging loads on cable ampacity and temperature in presence of different levels harmonic currents under two typical scenarios of EV charging modes, i.e., uncontrolled domestic charging scenario and uncontrolled off-peak domestic charging. The proposed method determines the ampacity and temperature of three-core cable taking account for cable geometry, harmonic levels, stochastic nature of the charging power, start time of individual charging, seasonal temperature under different EV penetrations. A generic urban distribution network with residential load ratio of 0.7, which is representative of urban residential distribution network in China, is adopted as an example. Results show that the ampacity of a 400 mm2 10 kV cross-linked polyethylene (XLPE) cable decreased by 10.79 A and 2.03% when the harmonic level is at the maximum allowed limit as given in IEC 61000-3-2. Meanwhile, the ampacity decreased by 23.34 A and 4.09% at the maximum limit given in IEC 61000-3-4. In presence of 36.15% and 41.66% total harmonic distortion current which are the most serious harmonic distortion in the example network with 30% EV penetration, cable ampacity decreased by 42.86 A, 8.06% and 61.35 A, 11.35% respectively.

Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

Distribution Network Power Loss Analysis Considering Uncertainties in Distributed Generations

Distribution network loss analysis is crucial for the economic operation in residential distribution networks. The increasing level of distributed generation (DG) has considerably improved the overall sustainability but raised the uncertainty in system losses and exacerbated voltage profiles. This paper presents a nodal distribution loss analysis approach in which the losses induced by loads and DGs are calculated recursively. In order to characterize the uncertainty, the Latin hypercube sampling (LHS)-based approach is presented for obtaining DG output samples. Further, the LHS-based sampling and loss analysis methods are combined into a proposed stochastic framework for loss analysis, which takes into account the DG output uncertainty. Case studies on a 36-bus radial distribution network verified the stochastic loss analysis method. Compared with the simple random sampling method, the proposed LHS-based stochastic loss analysis method can reach the same accuracy level for nodal voltages and losses more efficiently.

Optimal power allocation for residential network in islanded microgrid using capacity market demand response approach

This paper proposes a new demand response algorithm for load shedding and direct load control in smart homes with the goal of lowering peak-to-average ratios and enhancing power balance. The suggested algorithm makes use of two-way communication between distribution agents and smart homes (customers) to provide real-time load control, calculate percentage reductions, and produce distributed load consumption factors. An agent-based residential distribution network with four customers who owned smart appliances was modeled in MATLAB/SIMULINK to validate the technique. The findings show that the residential network is capable of reacting swiftly to supply changes and managing the grid's power balance through distributed load reduction, load shifting, and/or load adjustment, avoiding or limiting load shedding in the event of supply shortages.

Control strategy for improving voltage quality in residential power distribution network consisting of roof-top photovoltaic-wind hybrid systems, battery storage and electric vehicles

In this paper, a combined voltage control scheme is presented for improving the voltage profile, fluctuations, and imbalance of balanced and unbalanced residential distribution networks with a high penetration level (100% penetration level) of rooftop photovoltaic-wind turbine hybrid generation systems (PVWHSs) and electric vehicles (EVs). The combined control scheme includes both local and central control methods. Local controls in the inverter of EVs and PVWHSs of homes try to maintain their point of connection (POC) voltage within the acceptable range. Then the central controller does a complementary action for voltage improvement, by remotely controlling a battery energy storage (BES) and the network on-load tap changer (OLTC).The proposed control scheme is tested in DIgSILENT PowerFactory15.1.7 environment based on the weather and daily load data (wind speed, solar radiation, temperature, active and reactive power consumption of residential homes) during a year with a 1-min resolution in a three-phase four-wire network. The obtained results demonstrate the desired performance of the proposed scheme on voltage profile improvement, reducing voltage fluctuations, decreasing voltage imbalances of the phases and reduced number of tap operations, in the face of fast growth of distributed energy resources (DERs) penetration in the residential networks.

Load Flattening and Voltage Regulation Using Plug-In Electric Vehicle's Storage Capacity With Vehicle Prioritization Using ANFIS

Plug-in electric vehicle (PEV) storage capacity is limited, and also, it depends on the battery energy level and trip timings. Hence, in order to effectively utilize PEV's storage capacity for grid support, smart charging and discharging control strategies are required. In this work, a new PEV control strategy is developed to achieve flat load profile and voltage regulation using PEV's storage capacity in an active residential distribution network. Both utility and PEV owner benefits (maximization of PEV usage and customer revenue) are given equal importance while scheduling PEVs for grid support. PEV prioritization is accomplished using an adaptive neuro-fuzzy inference system (ANFIS) with five decision variables. It has been assumed that the PEVs are available as per the scheduled timings, and vehicle prioritization may cause marginal shift in pre-scheduled times, but still target system on a chip is always ensured. During utilization of PEVs for load flattening, the voltage regulation at each bus, where PEVs are connected, is achieved by controlling active power transactions between the bus and PEVs. A multi-objective genetic algorithm is used to decide optimal power transaction between the grid and PEVs while maximizing PEV's storage exploitation without violating voltage limits. The proposed ANFIS prioritization (fixed rate) is compared with the variable-power-rate strategy in order to investigate the advantages of the proposed method.

Decentralized Charging Control of Electric Vehicles in Residential Distribution Networks

Electric vehicle (EV) charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV charging control scheme to achieve "valley-filling" (i.e., flattening demand profile during overnight charging), meanwhile meeting heterogeneous individual charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a non-separable objective function and strongly coupled inequality constraints. We propose a novel shrunken primal-dual subgradient (SPDS) algorithm to support the decentralized control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model.