Congestion Management In Distribution Networks Research Articles

Distributed Dynamic Tariff for Congestion Management in Distribution Networks Considering Temporal–Spatial Coordination of Electric Vehicles

Distributed Dynamic Tariff for Congestion Management in Distribution Networks Considering Temporal–Spatial Coordination of Electric Vehicles

Capacity limitation based local flexibility market for congestion management in distribution networks: Design and challenges

In this paper, five of the main design challenges of local flexibility markets (LFMs) for congestion management are identified and discussed based on desirable market properties from economics theory and literature review. The five main design challenges are low market liquidity, reliability, baselines, forecast errors at low aggregation levels, and the high cost of measurements. A comprehensive capacity-limitation based LFM design has been proposed to address the challenges and a simulation example is presented to illustrate the design. The proposed design facilitates market participants’ decision making by design elements that improve market liquidity, reliability and handling of forecast errors. Moreover, a new capacity limitation product is proposed that is not defined with respect to a baseline and does not require sub-meter measurements leading to lower costs and conflict-of-interest in delivery validation. Generic algorithms are also proposed for calculating utility and cost of the flexibility product. The proposed design and discussions can contribute to improving the inter-disciplinary area between engineering and economics while helping policy-makers and LFM stakeholders to further understand the problem from a multi-dimensional perspective.

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.

Coordination of dynamic tariff and scheduled reprofiling product for day-ahead congestion management of distribution networks

The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position.

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.

Robust dynamic tariff method for day-ahead congestion management of distribution networks

With the large-scale deployment of distributed energy resources (DERs) in distribution networks, network congestion could occur due to the non-coordinated operation of DERs. The dynamic tariff (DT) method as a decentralized day-ahead congestion management method has been widely studied. In the DT method, it is assumed that the distribution system operator (DSO) and aggregators use the same energy requirement parameters, which is impractical due to the DSO’s forecast error. The discrepancy between the DSO’s forecast parameters and aggregator’s accurate parameters leads to a certain level of uncertainty that needs to be handled when employing the DT method. Therefore, a robust DT method is proposed for day-ahead congestion management while dealing with the uncertainty in the DT framework. A three-level robust DT model is formulated to obtain a robust DT solution, based on which the network constraints are respected even in the worst-case scenario. Moreover, due to the nonconvexity of the three-level robust DT model, the robust DT model is reformulated as a two-level optimization model, and a heuristic solution method is developed to obtain the robust DT solution with an iterative procedure. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the effectiveness of the proposed robust DT method for day-ahead congestion management in distribution networks. The case study results demonstrate that the deterministic DT method may be ineffective due to the DSO’s forecast errors whereas the proposed robust DT method can resolve congestion efficiently under the uncertain condition.

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.

Two-tier demand response with flexible demand swap and transactive control for real-time congestion management in distribution networks

With increasing deployment of distributed energy resources, network congestion is becoming a major concern of distribution system operators for secure operation of distribution networks. Real-time congestion management is important as congestion may occur at real-time operation due to forecast errors and components failures. To alleviate real-time congestion within distribution networks, demand response based congestion management schemes have been proposed. However, customers’ requirements on energy rebound, i.e., periods and amounts of energy rebound, are not considered in the existing demand response based congestion management schemes. Moreover, customers’ willingness to provide flexibilities for congestion management is not taken into account either. To resolve these issues, this paper proposes a two-tier demand response scheme with flexible demand swap and transactive control for real-time congestion management in distribution networks. With flexible demand swap and swap market, real-time congestion can be efficiently solved. Customers are able to rebound energy in multiple periods and determine the amounts of energy rebound by themselves. The proposed transactive control based interactive mechanism between aggregators and customers considers customers’ willingness to provide flexibilities. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the proposed two-tier demand response scheme for real-time congestion in distribution networks. The case study results demonstrate that the proposed two-tier demand response scheme can resolve congestion efficiently while considering customers’ requirements on energy rebound and willingness to provide flexibilities for congestion management.

Evaluating flexibility values for congestion management in distribution networks within Dutch pilots

Decentralisation of electricity generation, and electrification of heating and transportation pose challenges for the distribution networks, such as possible network congestion. Network operators investigate alternatives for reinforcements. Flexibility through demand response (DR) is one of these alternatives. Four theoretical possibilities for flexibility as a solution for congestion management are presented, in relation to four pilot projects on congestion management in the Netherlands. This article evaluates these four pilot projects based on six evaluation criteria. The strengths and weaknesses of all pilots are addressed, and the results of the pilot projects are discussed.

Congestion Management in Distribution Networks With Asymmetric Block Offers

In current practice, the day-ahead market-clearing outcomes are not necessarily feasible for distribution networks, i.e., the network constraints might not be satisfied. Hence, the distribution system operator may consider an ex-post re-dispatch mechanism, exploiting potential flexibility of local distributed energy resources including demand response (DR) units. Many DR units have an inherent “rebound effect,” meaning a decrease in power demand (response) must be followed by an increase (rebound) or vice versa, due to their underlying physical properties. A naive re-dispatch mechanism relying on DR units with non-negligible rebound effect may fail, since those units may cause another congestion in the rebound period. We propose a mechanism, which models the rebound effect of DR units using asymmetric block offers—this way, those units offer their flexibility using two subsequent blocks (response and rebound), each one representing the load decrease/increase in a time period. We demonstrate that though linear approximations of optimal power flow (OPF) models as potential re-dispatch mechanisms are more computationally efficient, they can result in a different dispatch of the asymmetric blocks than an exact convex relaxation of an AC-OPF model, and therefore, must be used with caution.

Dynamic Tariff-Subsidy Method for PV and V2G Congestion Management in Distribution Networks

This paper proposes a dynamic tariff-subsidy (DTS) method for congestion management in distribution networks with high penetration of photovoltaics (PV), heat pumps and electric vehicles (EVs) with vehicle-to-grid (V2G) function. The DTS method is an extension of the dynamic tariff method proposed in the previous study. With the DTS, the regulation prices can be positive (tariff) or negative (subsidy). The study shows that the negative regulation price is necessary and very effective to solve congestion due to feed-in power flows, such as PVs and EVs in the V2G mode. In the study, dual decomposition of a convex quadratic model is proposed in addition to a conventional method for the DTS calculation. The case studies on the Roy Billinton test system demonstrate the efficacy of the DTS method for congestion management in distribution networks.

Real-Time Congestion Management in Distribution Networks by Flexible Demand Swap

In addition to the day-ahead congestion management in distribution networks, the real-time congestion management is very important because many unforeseen events can occur at the real operation time, e.g., loss of generation of distributed energy resources or inaccurate forecast of energy consumption or production. Flexibility service from demand will be a good option to solve the real-time congestions if the cost of activating the flexibility service is fully addressed. This paper proposes a new method, namely “swap,” to employ the flexibility service from electric vehicles and heat pumps for real time congestion management. The swap method can maintain the power balance of the system and avoid the imbalance cost of activating the flexibility service. An algorithm for forming swaps through optimal power flow and mixed integer linear programming is proposed to implement the swap method. Case studies were carried out to validate the efficacy of the proposed swap method for real time congestion management and the proposed algorithm for forming swaps. The settlement process for the swaps in different markets is analyzed.

Dynamic Subsidy Method for Congestion Management in Distribution Networks

Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network of the Roy Billinton test system with high penetration of electric vehicles and heat pumps. The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity to cheap energy prices and has no rebound effect.

Dynamic Power Tariff for Congestion Management in Distribution Networks

This paper proposes dynamic power tariff (DPT), a new concept for congestion management in distribution networks with high penetration of electric vehicles, and heat pumps. The DPT concept is proposed to overcome a drawback of the dynamic tariff (DT) method, i.e., DPT can replace the price sensitivity parameter in the DT method, which is relatively unrealistic in practice. Based on the control theory, a control model with two control loops, i.e., the power flow control and voltage control, is established to analyze the congestion management process by the DPT method. Furthermore, an iterative method based on distributed optimization is proposed to determine the DPT rates, which enables active participation of aggregators in the congestion management. The case studies demonstrate the efficacy of the DPT method for congestion management in distribution networks, and show its ability to save congestion management cost compared to the DT methods.

Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

This paper proposes a distributed optimization-based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles and heat pumps. The DDT method employs a decomposition-based optimization method to have aggregators explicitly participate in congestion management, which gives more certainty and transparency compared to the normal DT method. With the DDT method, aggregators reveal their final aggregated plan and respect the plan during operation. By establishing an equivalent overall optimization, it is proven that the DDT method is able to minimize the overall energy consumption cost and line loss cost, which is different from the previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization where challenges arise due to multiple congestion points, multiple types of flexible demands, and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks.

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.

Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks

The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate congestions that might occur in a distribution network with high penetration of distributed energy resources (DERs). Uncertainty management is required for the decentralized DT method because the DT is determined based on optimal day-ahead energy planning with forecasted parameters such as day-ahead energy prices and energy needs which might be different from the parameters used by aggregators. The uncertainty management is to quantify and mitigate the risk of the congestion when employing the DT method, which is achieved by firstly formulating the problem as a chance constrained two-level optimization and then solving the problem through an iterative procedure. Two case studies were conducted to demonstrate the efficacy of the uncertainty management of DT method.

Distribution Locational Marginal Pricing Through Quadratic Programming for Congestion Management in Distribution Networks

This paper presents the distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the aggregator optimization which may cause the decentralized congestion management by DLMP to fail. It is proven in this paper, using convex optimization theory, the aggregator's optimization problem through QP is strictly convex and has a unique solution. The Karush-Kuhn-Tucker (KKT) conditions and the unique solution of the aggregator optimization ensure that the centralized DSO optimization and the decentralized aggregator optimization converge. Case studies using a distribution network with high penetration of electric vehicles (EVs) and heat pumps (HPs) validate the equivalence of the two optimization setups, and the efficacy of the proposed DLMP through QP for congestion management.

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.