Distribution Network Congestion Research Articles

Optimal scheduling of blockchain-based virtual power plants considering distribution network congestion

The development of renewable energy has brought new challenges to the power system. Virtual power plant based on blockchain is an effective means to solve the problem of new energy consumption. Solving the internal risk of line overload or voltage over-limit of a virtual power plant can make it play a better role. This paper proposes a scheduling method for a virtual power plant based on blockchain, which can solve the congestion problem. Firstly, the scheduling process of a blockchain-based virtual power plant is designed. Secondly, it is discretized as the basis of the congestion management scheme. Thirdly, the price penalty is introduced into the scheduling process to solve the congestion problem. Finally, an example is designed for simulation. The results show that the method can solve the congestion problem while realizing the virtual power plant scheduling.

Customized Pricing Strategy for Households Based on Occupancy-Aided Load Disaggregation

Pricing strategies can be utilized to manage energy demand-supply imbalance and power distribution network congestion. With the application of smart meter technologies, the real-time consumption behavior of individual households can be studied to enhance the effectiveness of the formulated pricing strategy. Hence, in this paper, a customized pricing strategy based on non-intrusive load monitoring (NILM) techniques is proposed from the perspective of the energy retailer, aiming to incentivize households to change their load consumption patterns. First, the real-time occupancy state of each household is detected to identify the ability of the household to respond to the retail price. Second, the occupancy-aided factorial hidden Markov model (FHMM) is developed to facilitate the retailer to incorporate the real-time consumption behavior of households into pricing strategy formulation. Third, the real-time price elasticity of the consumption of each appliance within each household is mathematically modeled based on the real-time hidden state of each appliance. Numerical results indicate that the proposed pricing strategy is effective in managing the load consumption of households and assisting the retailer in increasing profitability.

Cooperative P2P Transaction Framework Between DSO and PMO Based on Consensus ADMM Against Path-Sharing Distribution Network Congestion

Cooperative P2P Transaction Framework Between DSO and PMO Based on Consensus ADMM Against Path-Sharing Distribution Network Congestion

Research on a two-stage power management system for electric vehicles

The large-scale application of electric vehicles that can be connected to the grid in the future will pose new challenges to the existing theories and methods of power system planning and operation. The disorderly charging behaviour of a large number of electric vehicles may bring significant negative effects on the safety and economic operation of the power system. Free charging of a large number of electric vehicles will increase the load of the power system, and even cause the phenomenon of line congestion, so that the active distribution network cannot operate normally. Based on the two-level power management system, this paper proposes a price elasticity matrix to reflect car owners' response to charging prices of charging stations at different locations, and designs an optimal real-time charging service fee pricing scheme for multiple charging stations to guide car owners' charging behaviour. Simulation results show that the proposed blocking scheduling strategy can effectively guide car owners to make reasonable charging plans while taking into account the benefits of charging facility operators, owners' charging costs and owners' satisfaction with the way they use electricity, solve the possible distribution network congestion caused by large-scale electric vehicles' centralized charging in the network and improve the safety and economy of power grid operation.

A Transactive Energy Framework for Inverter-Based HVAC Loads in a Real-Time Local Electricity Market Considering Distributed Energy Resources

Rapidly increasing distributed energy resources (DERs) bring more fluctuating output power to the distribution network and put forward a higher requirement on local regulation resources for maintaining the network's balance. Heating, ventilation, and air conditioning (HVAC) loads account for more than 40% of power consumption in modern cities and have huge regulation potential as flexible loads. However, HVACs equipped with inverter devices have rarely been studied for providing regulation services in the local electricity market (LEM), even though they have exceeded regular fixed-speed HVACs. To address this issue, this article proposes a real-time LEM and a distribution network's optimization framework to exploit the regulation potential of inverter-based HVACs considering multiple DERs. This LEM can avoid iterations in real time and significantly decrease the difficulty related to the participation of small end-users in urban distribution networks. Moreover, in this article, we propose a transactive capacity evaluation method to assist end-users in deciding their inverter-based HVACs regulation capacities in the real-time LEM, which considers buildings’ thermal features, users’ multiple comfort requirements, and dynamic ambient temperature. On this basis, a multilevel bidding strategy is developed for inverter-based HVACs to decrease energy cost, increase fluctuating DERs local utilization rate, and alleviate the distribution network's congestion. Finally, a realistic distribution network is utilized to verify the effectiveness of the proposed methods.

Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility

This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexibility offered by electric vehicles, space heating and flexible loads in a partially observable stochastic environment. In the standard independent Q-learning approach, the coordination performance of agents under partial observability drops at scale in stochastic environments. Here, the novel combination of learning from off-line convex optimisations on historical data and isolating marginal contributions to total rewards in reward signals increases stability and performance at scale. Using fixed-size Q-tables, prosumers are able to assess their marginal impact on total system objectives without sharing personal data either with each other or with a central coordinator. Case studies are used to assess the fitness of different combinations of exploration sources, reward definitions, and multi-agent learning frameworks. It is demonstrated that the proposed strategies create value at individual and system levels thanks to reductions in the costs of energy imports, losses, distribution network congestion, battery depreciation and greenhouse gas emissions.

Spatial Effects of Flexibility Activation–Case Study of a Real-Life Hungarian Distribution Network

As distributed energy sources, energy storage and electric vehicles are spreading, active consumers have appeared, flexibility is playing an increasingly important role in tackling emerging distribution network congestion problems. Applying flexibility can be a cost-effective way to manage such challenges, and it can help to defer grid developments. The purpose of this paper is to present a modeling framework that can help test the operation of flexibility products that may be applied to a distribution network. The load flow based simulations using representative sample networks from Hungary were aimed at solving two selected network problems by a flexibility service, mapping a potential product providing compensation for voltage drop on medium voltage overhead lines and reducing load on the substation’s transformer. As a result of the simulations, spatial contribution factors according to topological location were determined based on sensitivity, and their variability was studied by time sweep analysis on the two different products. A scenario considered the appearance of distributed generation and showed the effects on contribution factors. The variation of the contribution factors due to different loading and distributed generation differs by an order of magnitude in the two different products, due to the parameters of the overhead medium voltage networks. This means that in some use cases – as this long overhead medium voltage line’s voltage drop issue is a practical example – the variation of spatial effects due to different loading is not negligible, which should be considered in the development of flexibility need and activation calculations.

Day‐ahead electricity pricing mechanism considering the conflict between distribution network congestion and renewable produce

There is practically no published work that considers distribution network use of system (DUoS) charges as a price signal in the day ahead-market, thus failing to establish a trade-off between the DUoS charges and the energy procurement costs. This paper presents a new approach to optimal design of a day-ahead pricing mechanism for managing distribution network congestion caused by renewable generation. The proposed concept of day-ahead pricing is designed to minimize customer electricity bills taking account of line power constraints resulting from increased penetration of renewable generation. It is assumed that each user is equipped with controllable appliances and a smart meter enabling two-way communications between suppliers and their customers. Suppliers employ an hourly demand side management strategy which considers the price signal from distribution network operators (DNOs), distributed generations (DGs) and wholesale market. A weighted average DUoS charge approach is applied in this paper, allowing DNOs to send a cost reflective use of system charge to electricity users in different locations based on their hourly predicted day-ahead power consumption. An optimum day-ahead pricing mechanism has been developed for DGs to manage their renewable resources to achieve revenue reconciliation and maximize generation output. Finally, power suppliers can optimally manage the controllable appliances that installed in each end user and balance the energy consumption from the main grid and DGs to minimize the electricity price. Several case studies have been conducted with a modified IEEE-33 bus distribution network, where a large-scale nonlinear optimization programming algorithm is applied to solve the problem.

Distribution network congestion management considering time sequence of peer-to-peer energy trading

In the deregulated energy market environment, small-scale peer-to-peer (P2P) energy trading can increase the distributed photovoltaic power generation consumption and promote local energy balance. However, it also increases the possibility of security constraints violations during the operation of the utility grid. To solve physical network congestion caused by distributed P2P energy trading, we propose a method that considers the time sequence of P2P energy trading based on a continuous double auction (CDA) mechanism. In addition, to make full use of the users’ flexibility to solve the network congestion, a two-tier market (P2P energy market and ancillary service market) coordination mechanism is proposed. The congestion management model includes two parts. One is congestion responsibility determination and congestion cost allocation in the P2P energy market, and the other is flexible services purchasing in the ancillary service market for minimizing congestion costs. The cost and benefit constraints are specially added in the congestion management model to reduce the economic loss of the grid and users. The proposed method is tested on 11-bus and IEEE 33-bus radial distribution systems to illustrate the effectiveness and scalability of this method. The simulation results show that the proposed mechanism and model effectively perform congestion management considering distributed P2P energy trading, and the results are profitable to users participating in both markets.

Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing

The growing demand for the fast charging of electric vehicles has created stronger interdependency between the operation of urban traffic networks and power distribution networks. This paper investigates the necessity and feasible mechanism of coordinated pricing in coupled urban power-traffic networks. The retail charging pricing of the power distribution network operator and congestion pricing of the urban traffic network operator will simultaneously affect the routing and charging decision choices for electric vehicles. Then, the response of multiclass vehicles—i.e., the aggregated traffic flow and charging load—will in return affect the operation performance—i.e., power loss and traffic congestion—of the coupled networks. However, due to operators’ differing pricing goals and limited access to user information, the actual response for vehicles might deviate from the respective expectation of the two operators under isolated pricing. With the growing penetration of electric vehicles, such deviation might become more evident, calling for a focus on pricing coordination. To investigate the necessity of coordinated pricing, the Stackelberg game framework is employed to model the pricing interactions between operators (leaders) and vehicles (followers). Accounting for privacy issues, a decentralized coordination framework of the two networks is proposed for information sharing. The numerical test results confirm the negative impacts of isolated pricing and demonstrate the effectiveness of coordinated pricing. Sensitivity analysis is conducted to examine the influence of critical parameters: e.g., charging electric vehicle penetration and total travel demand.

Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems

This paper proposes a novel decentralised control approach dedicated to coordinating the operation of a large population of residential thermal energy storage characterised by diversified specifications, while innovatively considering the operational constraints on both the national and the local level. The presented iterative algorithm sequentially updates the storage operational schedule under the real-time electricity price scheme to achieve cost savings for each individual participant and the total system, while effectively avoiding the violation of local distribution network restrictions. The transmission topology is explicitly considered to investigate the impacts of transmission congestion on the electricity marginal prices. The results of a series of case studies demonstrate that the proposed coordination approach can effectively enable individual energy arbitrage and achieve 22.53 % system cost savings compared to the 10.34 % savings when coordination is absent. Moreover, the simulation results manifest that the coordinated control approach can cost-effectively perform distribution congestion management to ensure no local network is overloaded, which will cause 12.3 % increase in system operational costs. Overall, through coordinating the operation of numerous residential thermal energy storage to perform both energy arbitrage and distribution network congestion management, the proposed control approach can benefit the system operation at both the national and local levels.

Optimal Operation of Energy Hubs With Large-Scale Distributed Energy Resources for Distribution Network Congestion Management

Congestion problems might occur in distribution networks as the penetration of distributed energy resources (DERs) progresses. This study focuses on the complementarity of multiple energy resources in energy hubs (EHs) to solve possible distribution network congestions. First, we consider an EH in which combined cooling, heating and power (CCHP) units and heat pumps are integrated with renewable energy resources. The EH models the intrinsic coupling relationship among various energy carriers, forming a flexible operation where electricity, natural gas, cooling and heat can complement each other. Next, an optimal operation strategy of multiple energy hubs enables gas-to-electricity to provide local energy supply for EH during electricity peak periods, and consume renewable energy generation by the complementarity of electricity, heat and cooling. In addition, the uncertainty of renewable energy resources is taken into account via scenario-based stochastic programming. Numerical results show that the proposed operation strategy explores EHs’ operation flexibility to mitigate distribution network congestion when we consider high renewable energy penetration in power systems.

Review of Congestion Management Methods for Power Systems

With the acceleration of China’s new power reform, the grid congestion problem has become more and more prominent. It has been a hot spot of power industry that studying on the appropriate grid congestion management method of electricity market in every country or region, for ensuring the safe and economic operation of the power system. In recent years, many great researches have been published on grid congestion management. This paper reviews the literature on grid congestion management through two aspects: transmission network congestion and distribution network congestion. Besides, dynamic congestion, congestion pricing and congestion allocation are described by summarizing various publications. Finally based on the reviews of the existing methods for grid congestion, recommendations for transmission and distribution congestion management methods that are suitable for Chinese electricity are suggested market in the end.

Distribution Network Congestion Dispatch Considering Time-Spatial Diversion of Electric Vehicles Charging

With the popularization of electric vehicles, free charging behaviors of electric vehicle owners can lead to uncertainty about charging in both time and space. A time-spatial dispatching strategy for the distribution network guided by electric vehicle charging fees is proposed in this paper, which aims to solve the network congestion problem caused by the unrestrained and free charging behaviors of large numbers of electric vehicles. In this strategy, congestion severity of different lines is analyzed and the relationship between the congested lines and the charging stations is clarified. A price elastic matrix is introduced to reflect the degree of owners’ response to the charging prices. A pricing scheme for optimal real-time charging fees for multiple charging stations is designed according to the congestion severity of the lines and the charging power of the related charging stations. Charging price at different charging station at different time is different, it can influence the charging behaviors of vehicle owners. The simulation results confirmed that the proposed congestion dispatching strategy considers the earnings of the operators, charging cost to the owners and the satisfaction of the owners. Moreover, the strategy can influence owners to make judicious charging plans that help to solve congestion problems in the network and improve the safety and economy of the power grid.

Hierarchical and distributed control concept for distribution network congestion management

Congestion management is one of the core enablers of smart distribution systems where distributed energy resources are utilised in network control to enable cost-effective network interconnection of distributed generation (DG) and better utilisation of network assets. The primary aim of congestion management is to prevent voltage violations and network overloading. Congestion management algorithms can also be used to optimise the network state. This study proposes a hierarchical and distributed congestion management concept for future distribution networks having large-scale DG and other controllable resources in MV and LV networks. The control concept aims at operating the network at minimum costs while retaining an acceptable network state. The hierarchy consists of three levels: primary controllers operate based on local measurements, secondary control optimises the set points of the primary controllers in real-time and tertiary control utilises load and production forecasts as its inputs and realises network reconfiguration algorithm and connection to the market. Primary controllers are located at the connection point of the controllable resource, secondary controllers at primary and secondary substations and tertiary control at the control centre. Hence, the control is spatially distributed and operates in different time frames.

Value of corrective network security for distributed energy storage applications

Energy storage can provide services to several sectors in electricity industry, including generation, transmission and distribution, where conflicts and synergies may arise when storage is used to manage network congestion and provide services in energy and balancing markets. In this context, this study proposes an optimisation model to coordinate multiple services delivered to various market participants that uses corrective actions to resolve conflicts between provision of distribution network services (e.g. congestion and security of supply) and other services. The model maximises storage profit by scheduling active and reactive power to provide portfolio of services including distribution network congestion management, energy price arbitrage, frequency response and reserve services remunerated at different prices. The authors demonstrate that adopting corrective security to provide network services and deal with network congestion in a post-fault fashion, is overall more beneficial despite the energy needed to be stored during pre-fault conditions for applying post-contingency actions right after a network fault occurs. Furthermore, the authors' analysis shows that application of corrective security can benefit both (i) storage owners through increased revenues in energy and balancing services markets and (ii) Distribution Network Operators through reduction in payments to storage owners and increased utilisation of network infrastructure.

A MILP model for optimising multi-service portfolios of distributed energy storage

Energy storage has the potential to provide multiple services to several sectors in electricity industry and thus support activities related to generation, network and system operation. Hence aggregating the value delivered by storage to these sectors is paramount for promoting its efficient deployment in the near future, which will provide the level of flexibility needed to deal with the envisaged high renewables share and the increase in peak demand driven by transport and heating electrification. In this context, we develop a Mixed Integer Linear Programming (MILP) model to schedule operation of distributed storage by coordinating provision of a range of system services which are rewarded at different market prices. The model maximises distributed storage’s net profit while providing distribution network congestion management, energy price arbitrage and various reserve and frequency regulation services through both active and reactive power control. We demonstrate benefits associated with the coordination of these services and its impacts on commercial strategies to determine optimal multi-service portfolios in the long term. We also demonstrate the value of reactive power control to support not only distribution network congestion management, but also efficient trading of energy and balancing services which are usually treated through active power-only control. In addition, we use the model to price the service of distribution network congestion management and propose an efficient investment policy to upgrade distribution network capacity in the presence of distributed storage. Finally, several case studies under current market conditions in Great Britain (GB) demonstrate that distributed storage revenues associated with frequency control services are significantly more profitable.

Analysis of traffic congestion costs in a competitive supply chain

We study a competitive facility location problem with identical suppliers competing in multiple markets subject to distribution network congestion. We first analyze the solution of a symmetric competitive location game and discuss the firms’ facility location and equilibrium market supply decisions. Then, we utilize the model to provide analytical characterization of the effects of traffic congestion costs on equilibrium distribution flows. These analytical results permit qualitative characterization of how changes in congestion levels and costs affect equilibrium location and distribution decisions. The results of extensive numerical studies serve to further illustrate the effects of traffic congestion costs on location, market supply quantity, and distribution decisions.