Distribution Grid Congestion Research Articles

Leveraging Prosumer Flexibility to Mitigate Grid Congestion in Future Power Distribution Grids

Leveraging Prosumer Flexibility to Mitigate Grid Congestion in Future Power Distribution Grids

Enhancing Distribution Grid Efficiency and Congestion Management through Optimal Battery Storage and Power Flow Modeling

The significant growth in demand for electricity has led to increasing congestion on distribution networks. The challenge is twofold: it is needed to expand and modernize our grid to meet this increased demand but also to implement smart grid technologies to improve the efficiency and reliability of electricity distribution. In order to mitigate these congestions, novel approaches by using flexibility sources such as battery energy storage can be used. This involves the use of battery storage systems to absorb excess energy at times of low demand and release it at peak times, effectively balancing the load and reducing the stress on the grid. In this paper, two optimal power flow formulations are discussed: the branch flow model (non-convex) and the relaxed bus injection model (convex). These formulations determine the optimal operation of the flexibility sources, i.e., battery energy storage, with the objective of minimizing power losses while avoiding congestions. Furthermore, a comparison of the performance of these two formulations is performed, analyzing the objective function results and the flexibility operation. For this purpose, a real Spanish distribution network with its corresponding load data for seven days has been used.

Impact of electric vehicle charging demand on power distribution grid congestion

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

From “Fit and Forget” to “Flex or Regret” in Distribution Grids: Dealing With Congestion in European Distribution Grids

From “Fit and Forget” to “Flex or Regret” in Distribution Grids: Dealing With Congestion in European Distribution Grids

Power Congestion Management in Integrated Electricity and Gas Distribution Grids

The proliferation of gas-fired generation (GfG) units and emerging power-to-gas (PtG) technology can set the stage for an integrated natural gas and power distribution system. PtG and gas-fired units have the potential to mitigate several existing and imminent issues of power distribution systems. This paper investigates how PtG and GfG facilities can be added to the portfolio of conventional resolutions when the motivation is (partly) congestion management in power distribution systems. It demonstrates how PtG and GfG units as merchant investments can change the operation philosophy from the traditional preventive to the novel corrective mode. To that end, a new real-time optimal scheduling algorithm is proposed to enable a PtG–GfG unit to optimally contribute to the congestion management. Merchandise operators profit, in addition to the arbitrage benefit, by relieving the distribution grid's congestion, thereby achieving a more stable return on investments. Slack variables are incorporated into the optimization problem to measure the contribution of the PtG–GfG unit to the congestion management. A new mechanism is proposed through which the merchandise operator is financially compensated by the power system operator, due to its contribution to the congestion management. Numerical studies using real-world data on a test system validate the efficacy and feasibility of the algorithm.

Bounds and limit theorems for a layered queueing model in electric vehicle charging

The rise of electric vehicles (EVs) is unstoppable due to factors such as the decreasing cost of batteries and various policy decisions. These vehicles need to be charged and will therefore cause congestion in local distribution grids in the future. Motivated by this, we consider a charging station with finitely many parking spaces, in which electric vehicles arrive in order to get charged. An EV has a random parking time and a random charging time. Both the charging rate per vehicle and the charging rate possible for the station are assumed to be limited. Thus, the charging rate of uncharged EVs depends on the number of cars charging simultaneously. This model leads to a layered queueing network in which parking spaces with EV chargers have a dual role, of a server (to cars) and a customer (to the grid). We are interested in the performance of the aforementioned model, focusing on the fraction of vehicles that get fully charged. To do so, we develop several bounds and asymptotic (fluid and diffusion) approximations for the vector process which describes the total number of EVs and the number of not fully charged EVs in the charging station, and we compare these bounds and approximations with numerical outcomes.

DSO-TSO cooperation issues and solutions for distribution grid congestion management

The role of DSOs is evolving due to the increasing penetration of intermittent and distributed energy resources in the distribution system. On the one hand, TSOs are accessing flexibility resources connected to the distribution grid. On the other hand, DSOs are actively managing distribution grid congestion, moving away from the conventional fit and forget approach. As a result, the need for DSO-TSO cooperation has become increasingly important. In this study, we first discuss market and grid operation issues related to different system states and the corresponding congestion management approaches, in the context of the European electricity market design and regulation. Second, we discuss viable solutions that are inspired by inter-TSO cooperation solutions as well as solutions that are being adopted by DSOs. Our findings show that the issues are rather similar both at transmission and distribution level; however, the need for cooperation and the solutions will depend on where structural congestion will occur and which borders will be managed. We also note that cooperation between DSOs as well as between DSOs and microgrids could become more important with the development of local energy markets in the long term.

Optimal Power Flow for Unbalanced Bipolar DC Distribution Grids

The emergence of distributed energy resources can lead to congestion in distribution grids. DC distribution grids are becoming more relevant as more sources and loads connected to the low voltage grid use dc. Bipolar dc distribution grids with asymmetric loading can experience partial congestion resulting in a nodal price difference between the two polarities if a respective market model is applied. In order to take into account this price difference, this paper presents an optimal power flow (OPF) model formulated in terms of voltage and current. In the case of bipolar dc distribution grids, the single line approximation is no longer valid because current can flow in the neutral conductors as well. Moreover, loads and sources can be connected between any two nodes in the network. The proposed exact OPF formulation includes bilinear equations. The locational marginal prices (LMP) are derived by linearizing the problem at the optimal solution. Example cases show the various phenomena that can appear under asymmetric loading, such as pole-to-pole connections combined with pole-to-neutral connections, parallel sources, meshed grids, and their effect on the LMP.

Distributed Congestion Management of Distribution Grids Under Robust Flexible Buildings Operations

Flexible demand side energy and reserve procurement have the potential to improve the overall operation of the grid. However, as argued in previous studies, this flexibility might cause congestion in distribution grids. In this paper, we improve the conventional distribution locational marginal price (DLMP) method, while integrating congestion free energy and reserve provision from buildings in distribution grids. First, robust day-ahead (DA) DLMPs are calculated to account for unmodeled dynamics of flexible loads. Second, using dual decomposition, the data sharing requirements between the aggregator and the distribution system operator are minimized. Third, a sensitivity-based real-time adjustment method is presented to remove the conservatism of DA robust DLMPs. Case studies are performed on a benchmark distribution system. The numerical results show that the proposed technique efficiently handles load uncertainties and data sharing requirements, improving the practicality of the conventional DMLP method.

Cost Optimal Integration of Flexible Buildings in Congested Distribution Grids

Buildings are candidates for providing flexible demand due to their high consumption and inherent thermal inertia. In the future, flexible demand side reserves may also help to relax the expected higher reserve requirements of the grid due to the presence of renewables. However, this flexible demand might be vulnerable to price signals, as the simultaneous increase in consumption by multiple buildings due to low (high) energy (reserves) price periods might cause congestion in distribution grids. In order to integrate congestion-free energy and reserve provision from buildings, this paper presents two benchmark pricing methodologies: (1 ) distribution locational marginal prices (DLMP), and (2) iterative DLMP (iDLMP). Both methods deploy convex optimization to obtain an optimal solution of the original problem. Using dual decomposition, a settlement scheme, which efficiently distributes the congestion cost among involved participants, is also presented. Case studies are performed on a benchmark distribution system along with the National Energy Market Singapore's price framework. The results prove that both methods optimally remove congestion from distribution grids and have potential to be integrated into the theoretical framework of liberalized markets. Furthermore, as a comparison, it is shown that the DLMP-based prices outperforms existing pricing structures of the distribution grid. Hence, using this scheme, the distribution system operator can evaluate existing tariffs and introduce incentives for price responsive demands. However, to support these methods, the high requirement for information sharing in the DLMP method and/or communication technology infrastructure for calculating iDLMPs must exist in the future grid.

DSO-TSO Cooperation Issues and Solutions for Distribution Grid Congestion Management

The role of DSOs is evolving due to the increasing penetration of intermittent and distributed energy resources in the distribution system. On the one hand, TSOs are accessing flexibility resources connected to the distribution grid. On the other hand, DSOs are actively managing distribution grid congestion, moving away from the conventional fit and forget approach. As a result, the need for DSO-TSO cooperation has become increasingly important. In this study, we first discuss market and grid operation issues related to different system states and the corresponding congestion management approaches. Second, we discuss possible solutions that are inspired by inter-TSO cooperation solutions as well as solutions that are being adopted by DSOs. Our findings show that the issues are rather similar both at transmission and distribution level; however, the need for cooperation and the solutions will depend on where structural congestion will occur and which borders will be managed.

Preventing Distribution Grid Congestion by Integrating Indirect Control in a Hierarchical Electric Vehicles’ Management System

In this paper, a hierarchical management system is proposed to integrate electric vehicles (EVs) into a distribution grid. Three types of actors are included in the system: distribution system operators (DSOs), fleet operators (FOs), and EV owners. In contrast to a typical hierarchical control system where the upper level controller directly controls the lower level subordinated nodes, this study aims to integrate two common indirect control methods: market-based control and price-based control into the hierarchical EVs’ management system. Specifically, on the lower level of the hierarchy, the FOs coordinate the charging behaviors of their EV users using a price-based control method. A parametric utility model is used on the lower level to characterize price elasticity of EVs and thus used by the FO to coordinate the individual EV charging. On the upper level of the hierarchy, the DSO uses the market-based control strategy to coordinate the limited power capacity of power transformer with FOs. To facilitate the application of the two indirect control methods into the system, a model describing decision tasks in control is used to specify the essential functions that are needed in the control system. The simulations illustrate the effectiveness of the proposed solutions.

A multi-agent system for distribution grid congestion management with electric vehicles

Electric vehicles (EVs) are widely regarded as valuable assets in the smart grid as distributed energy resources in addition to their primary transportation function. However, connecting EVs to the distribution network and recharging the EV batteries without any control may overload the transformers and cables during peak hours when the penetration of EVs is relatively high. In this study, a two level hierarchical control method for integrating EVs into the distribution network is proposed to coordinate the self-interests and operational constraints of two actors, the EV owner and Distribution system operator (DSO), facilitated by the introduction of the fleet operator (FO) and the grid capacity market operator (CMO). Unlike the typical hierarchical control system where the upper level controller commands the low level unit to execute the actions, in this study, market based control are applied both in the upper and low level of the hierarchical system. Specifically, in the upper level of the hierarchy, distribution system operator uses market based control to coordinate the fleet operator׳s power schedule. In the low level of the hierarchy, the fleet operator use market based control to allocate the charging power to the individual EVs, by using market based control, the proposed method considers the flexibility of EVs through the presence of the response-weighting factor to the shadow price sent out by the FO. Furthermore, to fully demonstrate the coordination behavior of the proposed control strategy, we built a multi-agent system (MAS) that is based on the co-simulation environment of JACK, Matlab and Simulink. A use case of the MAS and the results of running the system are presented to intuitively illustrate the effectiveness of the proposed solutions.

Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles

An economically efficient day-ahead tariff (DT) is proposed with the purpose of preventing distribution grid congestion resulting from electric vehicle (EV) charging scheduled on a day-ahead basis. The DT concept developed herein is derived from the locational marginal price (LMP), in particular the congestion cost component of the LMP. A step-wise congestion management structure has been developed whereby the distribution system operator (DSO) predicts congestion for the coming day and publishes DTs prior to the clearing of the day-ahead market. EV fleet operators (FOs) optimize their EV charging schedules with respect to the predicted day-ahead prices and the published DTs, thereby avoiding congestion while still minimizing the charging cost. A Danish 400V distribution network is used to conduct case studies to illustrate the effectiveness of the developed concept for the prevention of distribution grid congestion from EV charging. The case study results show that the concept is successful in a number of situations, most notably a predicted system overload of 155% can be successfully alleviated on the test distribution network.

Allowing for Off-Peak Price Incentives to Reduce Electric Vehicle Distribution Grid Congestion

Maryland legislators have set in motion financial investments, tax incentives and Public Service Commission directives to encourage and accommodate electric vehicle market expansion in the state. To combat potential capacity reliability disruptions caused by EVs, electric utilities must begin to design residential electricity price signals that will incentivize EV users to plug in their vehicles off-peak, where demand congestions are least likely to occur.

Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints

In this paper, the basic functions of an electric vehicle charging service provider are described with a focus on the associated optimization problems. A novel method of planning the charging of electric drive vehicles including electricity grid constraints, both voltage and power, is shown. The method establishes an individual charging plan for each vehicle and avoids distribution grid congestion while satisfying the requirements of the individual vehicle owners. The concepts proposed in this paper are tested on a simulated electricity grid. It is shown that both power and voltage constraints due to electric vehicle charging can be avoided using the proposed method.

RealTime distributed congestion control for electrical vehicle charging

The significant load and unpredictable mobility of electric vehicles (EVs) makes them a challenge for grid distribution systems. Unlike most current approaches to control EV charging, which construct optimal charging schedules by predicting EV state of charge and future behaviour, we leverage the anticipated widespread deployment of measurement and control points to propose an alternative vision. In our approach, drawing from a comparative analysis of Internet and distribution grid congestion, control actions taken by a charger every few milliseconds in response to congestion signals allow it to rapidly reduce its charging rate to avoid grid congestion. We sketch three control schemes that embody this vision and compare their relative merits and demerits.