Distribution Network Reinforcement Research Articles

Comparative techno-economic analysis of distribution network reinforcement and LEC flexibility for overvoltage regulation

Comparative techno-economic analysis of distribution network reinforcement and LEC flexibility for overvoltage regulation

PV/PV-Battery hosting capacity estimation method based on hidden Markov model for effective stochastic computation

Monte Carlo is commonly applied to model uncertainties in the penetration of photovoltaic (PV) systems as random processes. However, Monte Carlo simulations require a large number of stochastic calculations to obtain the desired accuracy. This paper develops a hosting capacity estimation model using hidden Markov to provide an effective stochastic calculation of PV/PV-battery penetration. To improve the representative in modeling actual penetration scenarios, the proposed model considers the probabilities among candidates on the basis of the customer types, the customer with PV-only or the customer with PV-battery, and the size of the PV/PV-battery. To be used in the simulations, a technique is proposed to calculate the min–max load demand and PV generation curves. To assess the computational load of the proposed model, this work provides an accuracy evaluation with respect to the number of stochastic simulations. The findings indicate that the proposed solution can achieve a cost-effective calculation of the hosting capacity. In practice, this work can provide the distribution planner with useful direction to help make informed decisions about the distribution network reinforcement strategy to deal with high PV/PV-battery penetration.

Probabilistic operational costs assessment of combined PV–PEV connections in LV distribution networks

Decarbonization stimulates the increased penetration of photovoltaic (PV) systems and plug-in electric vehicles (PEV) in low-voltage (LV) distribution networks. However, the individual or combined PV and PEV massive connections may incur significant economic costs for distribution utilities due to technical issues caused by these connections. This paper proposes a probabilistic approach for operational costs assessment of combined PV–PEV penetration in LV distribution networks catering to the uncertainties associated with PV, PEV and households. In addition, to identify the relationships between the technical issues with each other and the increased combined PV and PEV penetration levels, a correlation analysis is developed to help utility engineers to estimate the operational costs of combined PV and PEV connections. The proposed methodology is applied to a real Brazilian LV distribution network showing that the costs of real power losses decrease in reduced penetration levels of combined PV–PEV. However, they increase under high combined penetration levels representing financial losses to utilities. Also, operational costs related to steady-state voltage level compliance indicators increase with PV connection, and the combined PV–PEV connections have beneficial results, reducing these costs. Further, the proposed methodology can be used by utility engineers responsible for studies of LV distribution network reinforcement actions. • Proposed methodology converts technical parameters into operational costs. • Operational costs in utility’s point-of-view due to combined PV–PEV connection. • Proposed approach considering stochastic uncertainties of PV, PEV and households. • Identifies high-risk consumer units, allowing LV network reinforcements. • Proposed methodology uses technical correlation analysis and Monte Carlo Simulation.

A seismic-resilient multi-level framework for distribution network reinforcement planning considering renewable-based multi-microgrids

In this paper, a new resilient framework is presented for distribution network reinforcement planning (DNRP) with the consideration of renewable-based multi-microgrids (RMGs) to mitigate seismic risks. For this aim, the proposed framework is formulated as a non-convex mixed-integer nonlinear four-level optimization problem. The first level outlines a short-term corrective measures problem to remedy seismic risks using feeder reconfiguration and distributed energy resources rescheduling. The second level represents an earthquake-related catastrophic failures problem in which peak ground acceleration and distribution component vulnerability are modeled through attenuation functions and fragility curves, respectively. The third and fourth levels, however, describe coordination of a decentralized double-layer multistage microgrid reinforcement planning (MGRP) problem and a centralized cost-effective multistage DNRP problem to characterize seismic-resilient optimal reinforcement plans. Due to its desirable handling of multi-level optimization problems having a non-convex mixed-integer nonlinear nature, the multi-computational-step, multi-dimensional, multiple-homogeneous improved melody search algorithm, referred to as a symphony orchestra search algorithm (SOSA), is used to solve the proposed framework. The performance of the newly developed framework is numerically analyzed through implementing it to the standard 33-, 54-, 119- and 136-node distribution test networks. The numerical results well corroborate the sufficiency and profitableness of the proposed framework in achieving low vulnerability and high resilience to seismic risks.

Coordination of Distribution Network Reinforcement and DER Planning in Competitive Market

Since distributed energy resources (DERs) can provide capacity in distribution system, distribution network reinforcement interacts with DER planning (DERP) for meeting load growth. This article proposes the coordination of distribution network reinforcement and DER planning in competitive market. The model is formulated as a multi-stage programming. In first stage, distribution companies (DISCOs) and DER aggregators (DERAs) make bidding plans for network reinforcements and DERP. In second stage, DSO checks the network security. If the check fails, DSO sends capacity signals to DISCOs and DERAs for more capacities. In third stage, the distributed market is cleared by DSO’s optimal operation problem and distribution locational marginal price (DLMP) is returned to DERAs as price signal. An iterative algorithm is proposed to solve the multi-stage model. The algorithm embodies the process of information interaction among DISCOs, DERAs, and DSO, where DISCOs and DERAs compete in capacity market under DSO’s supervision. Numerical results on IEEE 33-system and an actual 141-bus system demonstrate that the proposed model coordinates distribution network reinforcement and DERP in competitive market.

Joint Expansion Planning Studies of EV Parking Lots Placement and Distribution Network

Long-term distribution network planning (DNP) is considered as one of the most challenging issues for distribution network operators (DNOs). By increasing EVs in cities, it has appeared some new players such as electric vehicles (EVs) owners and electric vehicle parking lots (EVPLs) owners for DNP. In this article, we conduct a new study on the coupled dynamic expansion problem of EVPLs placement and distribution networks. To reach this goal, at first, EVs driving and charging/discharging behavior as some influential factors is modeled using an efficient probabilistic algorithm. An analytical model is then introduced to estimate the number of EVs in EVPLs at different times. To find out the optimal place for installing EVPLs and distribution network reinforcement, a multiobjective (MO) optimization model is formulated considering the main requirements of EVs users, EVPLs owners, and distribution system operators. A linear model is also derived for the energy monument of EVs in EVPLs and residential charging stations to be used in the mentioned MO expansion problem. Numerical results with various cases are presented to illustrate the main advantages of the proposed coupled expansion model. The simulation results indicate increasing the percentage of participated EVs in V2G leads to reduce the DNO costs and increase the EVPLs’ revenues. Moreover, the year of feeder reinforcement will be deferred by considering V2G mode and battery degradation.

An innovative approach for distribution network reinforcement planning: Using DER flexibility to minimize investment under uncertainty

The increasing integration of Distributed Energy Resources (DER) in electricity networks has required an improvement in the network management procedures. While the operation paradigm is evolving and adapting to the new network features, the planning approach is rather inefficient as network assets are usually oversized to meet the worst-case scenario. In this regard, this paper presents an innovative methodology that integrates the potential flexibility of DER into the planning process, in an attempt to bridge the gap between current network operation approaches and the planning methods. It includes an analysis of future scenarios, providing different reinforcement plans considering the realistic network operation for those scenarios. The proposed optimal design of the reinforcement plans has two complementary processes: First to optimize flexible resources in their owner's perspective and second to reschedule the flexible resources’ operation when the DSO needs to solve technical problems. The model has been tested in a typical Portuguese medium voltage network using future scenarios of DER integration from ENTSO-E. The results conclude that the proposed methodology leads to cost-effective solutions, which provide a better use of flexible resources, deferring high capital investments in network reinforcement.

Increasing electric vehicle hosting capacity and equality for fast charging stations using residential photovoltaics in medium‐ and low‐voltage distribution networks

To achieve broad installation of electric vehicles (EVs), it is important to deploy fast EV charging stations at many points without distribution network reinforcement. This study develops a methodology for evaluating the hosting capacity for fast EV charging loads under medium‐ and low‐voltage (MV and LV) distribution networks with bus‐voltage and line‐current constraints; hence, the promising combination of fast EV charging and rooftop photovoltaic (PV) generation in distribution networks is analyzed. In particular, on a sunny day, the active power output supplied by rooftop PVs can be used to mitigate the voltage drop problem for end‐use residential customers. On a cloudy day, even though the active power output is smaller, the residual inverter capacities of rooftop PVs can be used to generate reactive power from those systems. In addition, in order to evaluate the spatial equalization of the EV hosting capacity at fast charging stations, an equalization factor of the spatial distribution is proposed, which is obtained as a percentage of the EV hosting capacity in the equally maximized case over that in the simply maximized case. The equalization factor can be improved from approximately 33 to 70% using the active and reactive power outputs from residential rooftop PVs. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Novel Strategy for Distribution Network Reinforcement Planning considering the Firm Capacity of Distributed Generation Units

Network reinforcement strategy has a critical role for the success in the planning process of Distribution Network Operators (DNOs). Plans of reinforcements should indicate, in details, each necessary modification in the network. In several regulatory environments, DNOs are the unique responsible for their network reinforcements decisions. Thus, the plan of reinforcements should be adherent to forecasted power flow, that should predict which branches/substations will be in an overload situation or with inappropriate voltages levels. Therefore, DNOs need develop reinforcement planning methodologies to consider important aspects as the forecast of power consumed by loads and the forecast of power generated by distributed generation units (DGs). In the literature, there are several studies focused on forecasting of load/distributed generation and network reinforcement strategies. In these studies, DGs location/size were considered independent variable. In most cases, authors consider that DNOs have DGs possession. However, it is not true in some regulatory environments, in which DGs location/size cannot be controlled by DNOs. Since in these cases, DNOs still are the unique responsible for their network reinforcements decisions. In this context, this paper proposes a new network reinforcement strategy able to consider the power generated by DGs and their location/size as stochastic variables into the DNOs reinforcements plan, using the concept of DGs firm capacity. The proposed methodology allows to correlate the necessary modifications in the network (and consequently the monetary value of these modifications) with the level of confidence intended by DNOs planners.

On demand: Can demand response live up to expectations in managing electricity systems?

Residential demand response (meaning changes to electricity use at specific times) has been proposed as an important part of the low carbon energy system transition. Modelling studies suggest benefits may include deferral of distribution network reinforcement, reduced curtailment of wind generation, and avoided investment in reserve generation. To accurately assess the contribution of demand response such studies must be supported by realistic assumptions on consumer participation. A systematic review of international evidence on trials, surveys and programmes of residential demand response suggests that it is important that these assumptions about demand response are not overly optimistic. Customer participation in trials and existing programmes is often 10% or less of the target population, while responses of consumers in existing schemes have varied considerably for a complex set of reasons. Relatively little evidence was identified for engagement with more dynamic forms of demand response, making its wider applicability uncertain. The evidence suggests that the high levels of demand response modelled in some future energy system scenarios may be more than a little optimistic. There is good evidence on the potential of some of the least ‘smart’ options, such as static peak pricing and load control, which are well established and proven. More research and greater empirical evidence is needed to establish the potential role of more innovative and dynamic forms of demand response.

Distribution network reinforcement planning in uncertain environment using stochastic multi-attribute utility analysis

This paper considers a stochastic multiple criteria decision-making problem in power distribution system reinforcement planning. The problem with exhaustive set of alternatives, with different types of trade-offs among stochastic criteria, has been solved using the novel analytical methodology. The proposed methodology is based on discrete convolution of criteria probability distribution functions and OWA operators, modelling different types of criteria aggregation. After the new, aggregated probability distributions have been built for every alternative, they are ranked by the overall expected utility. The methodology allows the usage of compensatory aggregation, which is more suitable for conflicting criteria or the human aggregation behaviour. Finally, an example of decision on the network reinforcement of IEEE 34 bus test feeder illustrates the efficiency of this method.

A Robust Model for Multiyear Distribution Network Reinforcement Planning Based on Information-Gap Decision Theory

This paper presents a new non-deterministic approach for multiyear distribution network reinforcement planning (DNRP) considering the uncertainty sources pertaining to loads, electricity prices, investment costs, and operation costs. Accordingly, the underlying idea of the information-gap decision theory (IGDT) is used to obtain a robust solution protected against different realizations of each uncertain variable lying in its robust region. The proposed model is capable of adjusting the robustness of the optimal solution in terms of a specific parameter designated as the budget of uncertainty. As the uncertain loads, investment and operation costs competitively tend to maximize their robust regions for a particular value of the budget of uncertainty, the normalized normal constraint (NNC) method as a proficient multi-objective optimization method is exploited in this paper to solve the proposed multiobjective IGDT-based DNRP (IGDT-DNRP) model. Mainly, the NNC method presents a set of Pareto optimal solutions rather than a single optimal solution. Accordingly, a posteriori out-of-sample analysis is introduced in this paper to find the best solution among the set of Pareto optimal solutions. The proposed IGDT-DNRP model is implemented on the IEEE 33-bus distribution network under different circumstances. Simulation results illustrate the effectiveness of the proposed nondeterministic approach.

Impacts of optimal energy storage deployment and network reconfiguration on renewable integration level in distribution systems

Nowadays, there is a wide consensus about integrating more renewable energy sources-RESs to solve a multitude of global concerns such as meeting an increasing demand for electricity, reducing energy security and heavy dependence on fossil fuels for energy production, and reducing the overall carbon footprint of power production. Framed in this context, the coordination of RES integration with energy storage systems (ESSs), along with the network’s switching capability and/or reinforcement, is expected to significantly improve system flexibility, thereby increasing the capability of the system in accommodating large-scale RES power. Hence, this paper presents a novel mechanism to quantify the impacts of network switching and/or reinforcement as well as deployment of ESSs on the level of renewable power integrated in the system. To carry out this analysis, a dynamic and multi-objective stochastic mixed integer linear programming (S-MILP) model is developed, which jointly takes the optimal deployment of RES-based DGs and ESSs into account in coordination with distribution network reinforcement and/or reconfiguration. The IEEE 119-bus test system is used as a case study. Numerical results clearly show the capability of ESS deployment in dramatically increasing the level of renewable DGs integrated in the system. Although case-dependent, the impact of network reconfiguration on RES power integration is not significant.

Impacts of direct load control on reinforcement costs in distribution networks

Based on the GreenLys Smart Grid experiment taking place in the cities of Grenoble and Lyon (France), this paper presents a quantitative method to evaluate the benefits of load control for the reinforcement costs of distribution networks. Preliminary results of this experimentation have led authors to develop a linear model that estimates the modification of electric demand caused by direct load control. The impact of demand control on the distribution network reinforcement costs is further discussed. The developed approach highlights the reinforcement costs savings potential of direct load control for urban areas.

Stochastic Analyses of Electric Vehicle Charging Impacts on Distribution Network

A stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed in this paper. Different from the previous deterministic approaches, the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data. Distribution operation security risk information, such as over-current and under-voltage, is obtained from three-phase distribution load flow studies that use stochastic parameters drawn from Roulette wheel selection. Voltage and congestion impact indicators are defined and a comparison of the deterministic and stochastic analytical approaches in providing information required in distribution network reinforcement planning is presented. Numerical results illustrate the capability of the proposed stochastic models in reflecting system losses and security impacts due to electric vehicle integrations. The effectiveness of a controlled charging algorithm aimed at relieving the system operation problem is also presented.

Optimal distribution network reinforcement considering load growth, line loss, and reliability

A new comprehensive planning methodology is proposed for implementing distribution network reinforcement. The load growth, voltage profile, distribution line loss, and reliability are considered in this procedure. A time-segmentation technique is employed to reduce the computational load. Options considered range from supporting the load growth using the traditional approach of upgrading the conventional equipment in the distribution network, through to the use of dispatchable distributed generators (DDG). The objective function is composed of the construction cost, loss cost and reliability cost. As constraints, the bus voltages and the feeder currents should be maintained within the standard level. The DDG output power should not be less than a ratio of its rated power because of efficiency. A hybrid optimization method, called modified discrete particle swarm optimization, is employed to solve this nonlinear and discrete optimization problem. A comparison is performed between the optimized solution based on planning of capacitors along with tap-changing transformer and line upgrading and when DDGs are included in the optimization.

The Environmental Benefits-Considered Cost Characteristics of Distributed Generation Analysis

The increasing environmental stress and energy crisis promote the distributed generation (DG) to integrate in the distribution network, thus cost characteristics of DGs are important to decision-makers for distribution network expansion and reinforcement. Firstly, this paper analyzed the fixed cost and variable cost of different types of DGs (such as micro-turbine, internal combustion engine, wind power, PV and fuel cell. and then drew the yearly cost characteristic curves. Secondly, we respectively calculated the relevant costs of thermal power generation and distributed generation. These costs include pollutant-emission cost and governance cost considering three types of pollutants, i.e. SO2, NOx and TSP. By doing this, we could better know the environmental benefits of DG compared with conventional power generation. Thirdly, we added the environmental benefits of distributed generation to the variable cost and regarded the final result as comprehensive variable cost. If reflects the environmental advantage of DG. Finally, we drew the cost characteristic curve with environmental benefit of DG. The research would provide reference when choosing the type and capacity of distributed generation in distribution network planning.

Smart control for minimizing distribution network reinforcement cost due to electrification

Electrification of transport fleets and heating sectors is seen as one of the key strategies to further reduce the use of fossil fuels and the resulting greenhouse gas emissions. However, it will potentially cause a significant increase of electricity peak demand and have adverse consequences on the electricity system, in particular on distribution networks. This paper will address the benefits of various applications of smart network control and demand response technologies for enhancing the integration of these future load categories, and for improvements in operation management and efficient use of distribution network assets. A range of numerical simulations have been carried out on different distribution network topologies (rural and urban networks) to identify the need and the cost of network reinforcement required to accommodate future load under various operating strategies such as Business as Usual (passive demand and passive network) against the smart grid approach. Applications of smart Plug-in vehicle (PiV) charging, smart heat pumps, and optimised control of network voltage regulators to reduce network investment have been studied, and selected key results of our studies on evaluating the benefits of implementing these technologies for Great Britain's distribution networks are presented and discussed in this paper.

Active voltage correction for industrial plants

Electric power supply voltage disturbances and, in particular, voltage sags, have been clearly identified as major problems to sophisticated continuous-process-based industries. Electricity network operators are often not able to provide the level of quality demanded by these industries at an economically viable cost using traditional distribution network reinforcement techniques. Considerable effort has been expended in the identification, quantification, and mitigation of voltage disturbances. Power electronic advancements have allowed the development of a new range of high-performance power conditioning equipment that can mitigate problems with the incoming power supply at the point of interface with the utility or within the plant internal electrical distribution system. This paper reviews the technologies available to perform high-speed voltage correction and discusses the results of practical application.