Grid Congestion Research Articles

Evaluating smart charging strategies using real-world data from optimized plugin electric vehicles

Management of electric vehicle (EV) charging is likely to be critical in avoiding large increases in peak electricity demand and subsequent build outs of generation and distribution infrastructure. Additionally, management of EV charging can help to more effectively utilize renewable energy resources. This study analyzed charging data from a real-world pilot program, the largest household-based study to date to analyze the effectiveness of different smart charging use cases at reducing charging costs and increasing utilization of renewable energy. The study included six different use cases that featured varying optimization signals and driver incentives. Study results suggest that the optimizations are effective at shifting load from times of high grid costs and congestion, most notably during early evening hours, to times of lower grid costs in the early morning and midday. The study finds that 15–20% of charge was shifted out of any given hour and 20–30% of charge was shifted into a given hour in the most effective use cases. In addition to shifting charging across time periods, charging events were also effectively shifted to different locations during some use cases, such as from overnight at households to during the day at workplaces.

Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system

Electric vehicles (EVs) have remarkably emerged as an alternative for internal combustion engines. With high penetration levels of the EVs, its addition to the existing distribution line infrastructure affects the power quality and grid stability. Additionally, EV charging loads are characteristically different as they are mobile compared to other fixed node-connected loads. Proposing solutions without considering these factors may result in grid congestion and subsequent over/under compensations. Another emerging dynamics co-occurring in the distribution system is high penetration levels of renewable energy in the utility grid. Although these dynamics pose challenges in stable grid operation, it also gives opportunities for solving some of the grid integration issues of EV loads. Thus, the effect of adding EV charging load to the existing low voltage distribution system must be analyzed by considering different criteria such as grid impact with different EV chargers, mobile nature of EV load, power quality, voltage profile, and spread/peak demand of load curve. This paper presents a detailed report on the impact analysis of EV integration on the component and system levels. For analyzing the effect on power quality (component level), grid pollution contributed by EV chargers along with possible solutions is elucidated. Detailed discussion on the significance of EV load location, existing load distribution, and nature of EV charging load distribution is presented by considering different feeders and different load curves. These discussions are supported with simulated case studies followed by a review of existing literature for enhanced understanding. Finally, techniques employed to effectively model the mobile nature of EV load and distributed EVs potential to provide ancillary solutions are also explained.

Edge and fog computing using IoT for direct load optimization and control with flexibility services for citizen energy communities

The grid congestion and imbalance problems are nowadays approached through a new perspective of the energy flexibility services that are usually acquired through a bidding process by aggregators on behalf of grid operators and electricity retailers and traded via Local Flexibility Markets (LFM). They can be also acquired at a fixed price under specific conditions of interruption and operation. The flexibility services are offered by electricity consumers and prosumers that own generation and storage facilities and controllable appliances, adding value to the emerging Information and Communications Technology (ICT) and smart metering technologies. In this paper, we propose an adaptive direct load optimization and control with an Internet of Things (IoT) architecture and compare it with a classic approach of Direct Load Control (DLC). The optimization process consists in a day-ahead scheduling that aims to minimize the electricity expense using programmable appliances (shift) and their operational constraints, whereas the adaptive DLC comes on top of it with additional load control (shed) using flexibility to cope with real-time uncertainties including the temperature comfort of the consumers. As the consumption is recorded using smart meters and appliances that continuously generate large volumes of data at different time resolution and formats requiring fast analytical and decisional processes, the challenge consists in correlating and integrating the optimization requirements with IoT and appliance control within an edge and fog computing architecture to overcome grid congestion, power capacity scarcity and forecast errors. The simulations are performed using real open datasets consisting in 114 single-family houses that form a small community with modern and flexible appliances, providing that the electricity bill reduction is up to 22.62%. Furthermore, to validate, several indicators for consumers and aggregator are proposed: total daily used flexibility decreased on average by 21.05%, number of interruptions also decreased on average by 20.51%, maximum number of interruptions per appliance decreased by 58.33%, while Peak to Average Ratio (PAR) improved by 32% when implementing the proposed DLC architecture.

Increasing installable photovoltaic power by implementing power-to-gas as electricity grid relief – A techno-economic assessment

The integration of renewable electricity generation poses challenges for today's energy system. Since renewable potentials are geographically unevenly distributed, solutions to overcome bottlenecks such as grid congestion are necessary to raise as much renewable potentials as possible. In specific, a part of Austria is characterised with high photovoltaic installation inquiries and low electricity demand. Electricity grids are designed to cope with today's loads, therefore the integration of inquired photovoltaic would overstrain the built infrastructure. In this paper we investigate how power-to-gas can ease this photovoltaic related grid strain. The feed-in of hydrogen into natural gas transmission pipelines and the usage of hydrogen and biogas from local fermentation plants for methanation feeding synthetic natural gas (SNG) into local distribution grid is assessed in various scenarios. Based on the technical assessment results, an economic evaluation of each scenario is performed to determine generation costs of hydrogen or SNG. Depending on the electrolysers location in the electricity grid, the installable photovoltaic power can be increased significantly without causing electricity grid congestion. Costs for hydrogen and SNG vary across each scenario, mainly influenced by CapEx for electrolyser and methanation as well as electricity purchasing costs.

Technical, Economic, Social and Regulatory Feasibility Evaluation of Dynamic Distribution Tariff Designs

The increasing number of distributed energy resources in the distribution grids creates the risk of grid congestion and the high cost of grid expansion. The implementation of the dynamic distribution grid tariffs can potentially avoid grid congestion. Meanwhile, the design and implementation of any distribution tariff need to consider and match the regional/national requirements. However, there is no sufficient evaluation method available to review and evaluate the feasibility of the dynamic distribution tariffs. Therefore, this paper introduces a feasibility evaluation method with four dimensions of technical, economic, social, and regulatory to review dynamic distribution tariffs. The literature on dynamic distribution tariffs is collected, and 29 dynamic distribution tariffs are selected and further categorized into five attributes of rationale, cost drivers, dynamics, events, and active demand. The evaluation results show that the time-of-use tariff is the most feasible dynamic distribution tariff, and the review of a proposed future distribution tariff model in Denmark verifies the evaluation method and results. The developed feasibility evaluation method for dynamic distribution tariffs can ensure the design and implementation of a dynamic distribution tariff to be feasible and applicable in a region.

Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

Optimal Design of an Isolated Hybrid Microgrid for Enhanced Deployment of Renewable Energy Sources in Saudi Arabia

Hybrid microgrids are presented as a solution to many electrical energetic problems. These microgrids contain some renewable energy sources such as photovoltaic (PV), wind and biomass, or a hybrid of these sources, in addition to storage systems. Using these microgrids in electric power generation has many advantages such as clean energy, stability in supplying power, reduced grid congestion and a new investment field. Despite all these microgrids advantages, they are not widely used due to some economic aspects. These aspects are represented in the net present cost (NPC) and the levelized cost of energy (LCOE). To handle these economic aspects, the proper microgrids configuration according to the quantity, quality and availability of the sustainable source of energy in installing the microgrid as well as the optimal design of the microgrid components should be investigated. The objective of this paper is to design an economic microgrid system for the Yanbu region of Saudi Arabia. This design aims to select the best microgrid configuration while minimizing both NPC and LCOE considering some technical conditions, including loss of power supply probability and availability index. The optimization algorithm used is Giza Pyramids Construction (GPC). To prove the GPC algorithm’s effectiveness in solving the studied optimization problem, artificial electric field and grey wolf optimizer algorithms are used for comparison purposes. The obtained results demonstrate that the best configuration for the selected area is a PV/biomass hybrid microgrid with a minimum NPC and LCOE of $319,219 and $0.208/kWh, respectively.

Liquefied Synthetic Natural Gas Produced through Renewable Energy Surplus: Impact Analysis on Vehicular Transportation by 2040 in Italy

Time mismatch between renewable energy production and consumption, grid congestion issues, and consequent production curtailment lead to the need for energy storage systems to allow for a greater renewable energy sources share in future energy scenarios. A power-to-liquefied synthetic natural gas system can be used to convert renewable energy surplus into fuel for heavy duty vehicles, coupling the electric and transportation sectors. The investigated system originates from power-to-gas technology, based on water electrolysis and CO2 methanation to produce a methane rich mixture containing H2, coupled with a low temperature gas upgrading section to meet the liquefied natural gas requirements. The process uses direct air CO2 capture to feed the methanation section; mol sieve dehydration and cryogenic distillation are implemented to produce a liquefied natural gas quality mixture. The utilization of this fuel in heavy duty vehicles can reduce greenhouse gases emissions if compared with diesel and natural gas, supporting the growth of renewable fuel consumption in an existing market. Here, the application of power-to-liquefied synthetic natural gas systems is investigated at a national level for Italy by 2040, assessing the number of plants to be installed in order to convert the curtailed energy, synthetic fuel production, and consequent avoided greenhouse gases emissions through well-to-wheel analysis. Finally, plant investment cost is preliminarily investigated.

Nodal dynamic charging price formulation for electric vehicle through the Stackelberg game considering grid congestion

The disordered charging of electric vehicles (EVs) may result in transmission congestion during the peak load period; therefore, a nodal dynamic charging price (NDCP) strategy is proposed to schedule the charging activity of regional electric vehicle agents (REVAs) to manage congestion. REVA is responsible for meeting the charging demand of users at a minimum charging cost, while the grid aims at minimising the congestion management cost and regulates the charging price of different nodes. The Stackelberg game theory is used to model the negotiation process of charging price, and the congestion contribution index is proposed to determine the congestion cost that the user side should be allocated. The proposed approach is implemented on a modified IEEE 30 bus system, and the results show that the congestion caused by EVs is reduced effectively, while the charging cost and congestion cost are also optimised.

Infrastructural coupling of the electricity and gas distribution grid to reduce renewable energy curtailment

Following the European Union’s emission reduction goals, the expansion of intermittent renewable energy sources is being pursued by numerous member states. This poses challenges especially to low-voltage electricity grids that are not designed for the volatile and unpredictable feed-in from renewable generation capacity. In addition to the expansion of renewable capacity, further measures, including the decarbonization of the transport, heating and industrial sectors are needed to achieve the environmental targets. Sector coupling refers to the electrification of end-user energy demand as well as the coupling of different energy infrastructures such as the electricity and gas networks through Power-to-Gas technology. In this paper, we address these issues by developing a methodology that enables distribution system operators to identify future grid constraints in advance and to address them using Power-to-Gas technology using geographical information systems. In further detail, we present a novel approach to identify sections of the distribution network that are likely to be congested in the future in order to locate congestion-induced potential sites for Power-to-Gas plants. We show the applicability of our approach in a case study for a municipality in the German state of Baden-Wurttemberg. We show the economic feasibility of a medium-sized Power-to-Gas plant that couples the gas and electricity distribution networks. Our findings offer insights into the possibility to use the existing gas infrastructure in order to integrate surplus electricity generation, avoid electricity grid congestion and to further decarbonize energy demand.

An Analysis of Multi Objective Energy Scheduling in PV-BESS System Under Prediction Uncertainty

Energy storage systems (ESSs) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion, degradation while maximizing self-consumption. As such, studies will be carried out, in this work, to highlight the improvement with MPC based energy management over conventional method using simulations of one-year system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and addressing the same through constraint tightening will be presented. It is concluded that MPC provides improvement in system behaviour over multiple performance criteria.

Power Trades and Network Congestion Externalities

Integrating renewable energy production into the electricity grid is an important policy goal to address climate change. However, such an integration faces economic and technological challenges. As power generation by renewable sources increases, power transmission patterns over the electric grid change. Due to physical laws, these new transmission patterns lead to non-intuitive grid congestion externalities. We derive the conditions under which negative network externalities due to power trades occur. Calibration using a stylized framework and data from Europe shows that each additional unit of power traded between northern and western Europe reduces transmission capacity for the southern and eastern regions by 27% per unit traded. Such externalities suggest that new investments in the electric grid infrastructure cannot be made piecemeal. In our example, power infrastructure investment in northern and western Europe needs an accompanying investment in southern and eastern Europe as well. An economic challenge is regions facing externalities do not always have the financial ability to invest in infrastructure. Power transit fares can help finance power infrastructure investment in regions facing network congestion externalities. The resulting investment in the overall electricity grid facilitates integration of renewable energy production.

Design of fractional evolutionary processing for reactive power planning with FACTS devices

Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap settings, generator voltages and reactive power sources, such as flexible alternating current transmission systems (FACTS). The optimal dispatch improves the system security, voltage profile, power transfer capability and overall network efficiency. In the present work, a fractional evolutionary approach achieves the desired objectives of reactive power planning by incorporating FACTS devices. Two compensation arrangements are possible: the shunt type compensation, through Static Var compensator (SVC) and the series compensation through the Thyristor controlled series compensator (TCSC). The fractional order Darwinian Particle Swarm Optimization (FO-DPSO) is implemented on the standard IEEE 30, IEEE 57 and IEEE 118 bus test systems. The power flow analysis is used for determining the location of TCSC, while the voltage collapse proximity indication (VCPI) method identifies the location of the SVC. The superiority of the FO-DPSO is demonstrated by comparing the results with those obtained by other techniques in terms of measure of central tendency, variation indices and time complexity.

Blockchain or Aggregator? An Assessment of Service Provision Approaches for Prosumer Engagement in Grid Congestion Management

Distributed load-management is seen as a means to enhance the integration of fluctuating renewable energy sources into the energy system and address the increasing strain on the German electricity grid. However, the advantages and disadvantages of different service provision approaches for this application, in particular blockchain-based solutions in contrast to aggregators, have not yet been investigated, a research avenue important to inform policymakers in the early stages of market and technology development. The sustainability and feasibility of three approaches, public blockchain, consortium blockchain and an aggregator using a relational database, were evaluated for their suitability to provide grid redispatch services through prosumer engagement, according to seven criteria by applying a multi-criteria analysis (MCA). The three feasibility criteria considered were scalability, regulatory acceptance and implementation network capacity, while the four sustainability criteria considered were privacy, security, costs and energy consumption. The analysis showed that public blockchain is least suitable for this application, whilst consortium blockchain and an aggregator using a relational database perform similarly on sustainability criteria, with the consortium blockchain performing better on security and the aggregator model on energy consumption. From a feasibility perspective, an aggregator using a relational database clearly outperformed blockchain solutions, suggesting better suitability at this point in time, with a need for regulatory innovation to allow for the future adoption of blockchain models.

Active Power Curtailment in Power System Planning

In regions with high penetration of Distributed Energy Resources (DER), grid congestion is mostly caused by power feed-in of generators. In this context, conventional approaches for solving the grid congestion problems are Active Power Curtailment (APC) of DER and grid reinforcement. In the wake of increased DER generation, it is not only of interest how to obtain the grid reinforcement, but also how to consider APC during grid planning. The challenge of such grid planning is to determine the best combination of reinforcement and APC. We present a new grid planning method and use it to find the trade-off between APC and grid reinforcement in a case study of a High-Voltage (HV) grid with a future renewable energy scenario. We compare the proposed reinforcement method to two existing methods. With the combination of APC and grid reinforcement, the proposed method shows a decrease regarding the total expenditures.

Coordination strategies in distribution network considering multiple aggregators and high penetration of electric vehicles

Given the current state of the society in which we live, in terms of energy pollution, several objectives have been set to try to reduce environmental problems. Some of these goals include an exponential increase in production through renewable energy, and Electric Vehicles (EVs) circulating on roads. Due to this high penetration of distributed energy resources in the electricity grid, several problems may exist: grid congestion, causing severe energy systems damage. Innovative coordination strategies must be developed to mitigate these situations. This paper proposes a methodology to minimize this problem in a smart grid with high penetration of Distributed Generation (DG) and EVs, taking into account multiple aggregators. Initially, the proposed model calculates each aggregator’s profit through some business models and analyzes the network without any congestion strategy. This analysis is then done in the presence of Distribution Locational Marginal Pricing (DLMPs), which the aggregator receives from the Distributed System Operator (DSO). The DSO gets these prices after running the Optimal Power Flow (OPF), where these prices involve the market price, the cost of losses, and the cost of congestion at a given point in the network. Here the aggregators react according to these costs, such as trying to buy flexibility from their customers. In this study, the results prove that dynamic prices are more viable for the power grid by reducing congestion by analyzing each aggregator’s profit.

The Impact of Uncertainty and Time Structure on Optimal Flexibility Scheduling in Active Distribution Networks

The authors focus on a model for system operators that uses centralized scheduling of multiple flexibility assets and services to minimize the cost of managing problems with grid congestion, voltages, and losses. The model schedules flexibility assets using stochastic optimization for AC optimal power flow in an active distribution network. The novelty of the contribution lies in its focus on how the dynamic capabilities of the flexibility resources are defined with regard to how uncertainty is resolved in the model. The impact of uncertainty is studied by using well-known quality measures from stochastic programming, such as the value of the stochastic solution. Moreover, the authors introduce a new measure related to the impact of representing uncertainty and flexibility when considering reactive power. By changing the time attributes of flexibility assets, the authors show the impact of uncertainty and time structure on a scheduling problem. The uncertainties considered are price and load levels. The findings reveal that the quality of the scheduling of each flexibility resource depends on using a stochastic model with a rigorous consideration of time and uncertainty.

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Sørensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids.

Fuzzy Dynamic Thermal Rating System-Based SIPS for Enhancing Transmission Line Security

The increased integrations of intermittent renewable energy sources into power systems cause more power grid congestions and therefore system operators need more advanced control to relieve this pressure. The dynamic thermal rating (DTR) system is able to increase the thermal constraint and subsequently maximum loading of existing lines. This dynamic rating is achieved through real-time considerations of weather data and it is usually much higher than the traditional static thermal rating system. The operational tripping scheme (OTS), a variant of the wider system integrity protection scheme, also relieves line congestions but it does this by tripping pre-selected generators and this have the unwanted consequence of reducing power adequacy. This paper proposes the novel integration of DTR and OTS, while considering the inherent uncertainties of their sensors based on fuzzy numbers, to avoid unnecessary generation tripping due to conservative line ratings. This novel Fuzzy-DTR-OTS delays the tripping of generations, enhances the adequacy of power supply, improves system security and avoids high risk cascading black out inducing events.

Cloud Electricity Storage for Multi-Service Battery Operation: Case of RTE

Problem definition: We study a cloud storage operator that provides shared storage service for electricity end-users using the residual part of a multi-service grid-scale battery primarily used for high-priority grid services. We design an optimal product with regard to pricing and customer portfolio. Academic/practical relevance: A framework and solution approach for assessing and operating such multi-service battery operations with stochastic services and different priority levels is an open problem. Methodology: We model the problem as a two-stage stochastic optimization between high-priority stochastic grid services and low-priority cloud storage for stochastic end-users. We also propose operational metrics of multiplexing gain and probability of blocking to assess the operation of the multi-service multi-user battery. To address the computational challenge of solving the stochastic optimization with a large number of end-users, we propose effective capacity as a convex approximation that allows an analytical solution. We then provide an empirical analysis based on real grid congestion data from RTE France and a large dataset of end-users' electricity consumption in California. Results: Our empirical analysis shows (i) our proposed effective capacity is a close approximation, (ii) battery operation and profit are sensitive to the cost of external resources, number of end-users, and the leasing price of the battery, and (iii) with only a slight discount of the leasing price (~1%), the profit of the third party from a stochastic residual battery can be the same as that from a deterministic one. Managerial implications: Cloud storage as a low-priority service can profitably exist alongside other high-priority battery services, making integration of more storage in the grid economically viable and allowing larger intermittent renewables, a key path towards reduced carbon emissions.